Overview

Dataset statistics

Number of variables46
Number of observations144226
Missing cells1618253
Missing cells (%)24.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory50.6 MiB
Average record size in memory368.0 B

Variable types

Numeric11
DateTime4
Categorical13
Text11
Unsupported7

Alerts

Complaint Type has constant value ""Constant
Descriptor has constant value ""Constant
Facility Type has constant value ""Constant
BBL is highly overall correlated with Borough and 7 other fieldsHigh correlation
Borough is highly overall correlated with BBL and 4 other fieldsHigh correlation
Borough Boundaries is highly overall correlated with BBL and 6 other fieldsHigh correlation
City is highly overall correlated with BBL and 3 other fieldsHigh correlation
Latitude is highly overall correlated with BBL and 3 other fieldsHigh correlation
Longitude is highly overall correlated with Park Borough and 2 other fieldsHigh correlation
Park Borough is highly overall correlated with BBL and 6 other fieldsHigh correlation
Police Precincts is highly overall correlated with BBL and 4 other fieldsHigh correlation
Resolution Description is highly overall correlated with StatusHigh correlation
Status is highly overall correlated with Resolution DescriptionHigh correlation
X Coordinate (State Plane) is highly overall correlated with Longitude and 1 other fieldsHigh correlation
Y Coordinate (State Plane) is highly overall correlated with BBL and 2 other fieldsHigh correlation
incident_zip is highly overall correlated with BBL and 4 other fieldsHigh correlation
zip_codes is highly overall correlated with incident_zipHigh correlation
Agency is highly imbalanced (> 99.9%)Imbalance
agency_name is highly imbalanced (> 99.9%)Imbalance
location_type is highly imbalanced (89.9%)Imbalance
Address Type is highly imbalanced (75.2%)Imbalance
closed_date has 77444 (53.7%) missing valuesMissing
location_type has 10523 (7.3%) missing valuesMissing
Incident Address has 2601 (1.8%) missing valuesMissing
Street Name has 2601 (1.8%) missing valuesMissing
Cross Street 1 has 16781 (11.6%) missing valuesMissing
Cross Street 2 has 15910 (11.0%) missing valuesMissing
Intersection Street 1 has 53208 (36.9%) missing valuesMissing
Intersection Street 2 has 52337 (36.3%) missing valuesMissing
Address Type has 15612 (10.8%) missing valuesMissing
City has 2297 (1.6%) missing valuesMissing
Landmark has 68746 (47.7%) missing valuesMissing
Facility Type has 144225 (> 99.9%) missing valuesMissing
Due Date has 78009 (54.1%) missing valuesMissing
Resolution Description has 10930 (7.6%) missing valuesMissing
Resolution Action Updated Date has 14307 (9.9%) missing valuesMissing
BBL has 21174 (14.7%) missing valuesMissing
X Coordinate (State Plane) has 2022 (1.4%) missing valuesMissing
Y Coordinate (State Plane) has 2016 (1.4%) missing valuesMissing
Vehicle Type has 144226 (100.0%) missing valuesMissing
Taxi Company Borough has 144226 (100.0%) missing valuesMissing
Taxi Pick Up Location has 144226 (100.0%) missing valuesMissing
Bridge Highway Name has 144226 (100.0%) missing valuesMissing
Bridge Highway Direction has 144226 (100.0%) missing valuesMissing
Road Ramp has 144226 (100.0%) missing valuesMissing
Bridge Highway Segment has 144226 (100.0%) missing valuesMissing
Latitude has 2022 (1.4%) missing valuesMissing
Longitude has 2022 (1.4%) missing valuesMissing
Location has 2022 (1.4%) missing valuesMissing
zip_codes has 2486 (1.7%) missing valuesMissing
Community Districts has 2034 (1.4%) missing valuesMissing
Borough Boundaries has 2034 (1.4%) missing valuesMissing
City Council Districts has 2034 (1.4%) missing valuesMissing
Police Precincts has 2034 (1.4%) missing valuesMissing
Unique Key has unique valuesUnique
Vehicle Type is an unsupported type, check if it needs cleaning or further analysisUnsupported
Taxi Company Borough is an unsupported type, check if it needs cleaning or further analysisUnsupported
Taxi Pick Up Location is an unsupported type, check if it needs cleaning or further analysisUnsupported
Bridge Highway Name is an unsupported type, check if it needs cleaning or further analysisUnsupported
Bridge Highway Direction is an unsupported type, check if it needs cleaning or further analysisUnsupported
Road Ramp is an unsupported type, check if it needs cleaning or further analysisUnsupported
Bridge Highway Segment is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-11-28 17:01:28.849506
Analysis finished2023-11-28 17:03:49.220983
Duration2 minutes and 20.37 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

Unique Key
Real number (ℝ)

UNIQUE 

Distinct144226
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45637677
Minimum32311150
Maximum59550214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-11-28T12:03:49.288187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum32311150
5-th percentile33396242
Q138332805
median44195880
Q354081986
95-th percentile58292800
Maximum59550214
Range27239064
Interquartile range (IQR)15749181

Descriptive statistics

Standard deviation8469109.6
Coefficient of variation (CV)0.18557276
Kurtosis-1.3853633
Mean45637677
Median Absolute Deviation (MAD)7657971
Skewness0.10098611
Sum6.5821396 × 1012
Variance7.1725817 × 1013
MonotonicityNot monotonic
2023-11-28T12:03:49.394856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59544918 1
 
< 0.1%
39915803 1
 
< 0.1%
39912840 1
 
< 0.1%
39912577 1
 
< 0.1%
39916765 1
 
< 0.1%
39911868 1
 
< 0.1%
39915845 1
 
< 0.1%
39911614 1
 
< 0.1%
39917908 1
 
< 0.1%
39913824 1
 
< 0.1%
Other values (144216) 144216
> 99.9%
ValueCountFrequency (%)
32311150 1
< 0.1%
32311162 1
< 0.1%
32311873 1
< 0.1%
32311944 1
< 0.1%
32312384 1
< 0.1%
32312385 1
< 0.1%
32312409 1
< 0.1%
32312809 1
< 0.1%
32314333 1
< 0.1%
32314570 1
< 0.1%
ValueCountFrequency (%)
59550214 1
< 0.1%
59550213 1
< 0.1%
59549163 1
< 0.1%
59549162 1
< 0.1%
59549161 1
< 0.1%
59548103 1
< 0.1%
59548102 1
< 0.1%
59548101 1
< 0.1%
59545988 1
< 0.1%
59545987 1
< 0.1%
Distinct135253
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Minimum2016-01-01 09:35:43
Maximum2023-11-25 20:28:12
2023-11-28T12:03:49.597726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:49.701129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

closed_date
Date

MISSING 

Distinct51583
Distinct (%)77.2%
Missing77444
Missing (%)53.7%
Memory size1.1 MiB
Minimum1899-12-31 19:00:00
Maximum2023-11-22 14:54:59
2023-11-28T12:03:49.798210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:49.897602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Agency
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
DPR
144225 
NYPD
 
1

Length

Max length4
Median length3
Mean length3.0000069
Min length3

Characters and Unicode

Total characters432679
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDPR
2nd rowDPR
3rd rowDPR
4th rowDPR
5th rowDPR

Common Values

ValueCountFrequency (%)
DPR 144225
> 99.9%
NYPD 1
 
< 0.1%

Length

2023-11-28T12:03:49.995268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T12:03:50.088428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
dpr 144225
> 99.9%
nypd 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
D 144226
33.3%
P 144226
33.3%
R 144225
33.3%
N 1
 
< 0.1%
Y 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 432679
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 144226
33.3%
P 144226
33.3%
R 144225
33.3%
N 1
 
< 0.1%
Y 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 432679
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 144226
33.3%
P 144226
33.3%
R 144225
33.3%
N 1
 
< 0.1%
Y 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 432679
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 144226
33.3%
P 144226
33.3%
R 144225
33.3%
N 1
 
< 0.1%
Y 1
 
< 0.1%

agency_name
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Department of Parks and Recreation
144225 
New York City Police Department
 
1

Length

Max length34
Median length34
Mean length33.999979
Min length31

Characters and Unicode

Total characters4903681
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDepartment of Parks and Recreation
2nd rowDepartment of Parks and Recreation
3rd rowDepartment of Parks and Recreation
4th rowDepartment of Parks and Recreation
5th rowDepartment of Parks and Recreation

Common Values

ValueCountFrequency (%)
Department of Parks and Recreation 144225
> 99.9%
New York City Police Department 1
 
< 0.1%

Length

2023-11-28T12:03:50.163729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T12:03:50.253261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
department 144226
20.0%
of 144225
20.0%
parks 144225
20.0%
and 144225
20.0%
recreation 144225
20.0%
new 1
 
< 0.1%
york 1
 
< 0.1%
city 1
 
< 0.1%
police 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
576904
11.8%
e 576904
11.8%
a 576901
11.8%
t 432678
 
8.8%
r 432677
 
8.8%
n 432676
 
8.8%
o 288452
 
5.9%
i 144227
 
2.9%
D 144226
 
2.9%
P 144226
 
2.9%
Other values (14) 1153810
23.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3894097
79.4%
Space Separator 576904
 
11.8%
Uppercase Letter 432680
 
8.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 576904
14.8%
a 576901
14.8%
t 432678
11.1%
r 432677
11.1%
n 432676
11.1%
o 288452
7.4%
i 144227
 
3.7%
c 144226
 
3.7%
k 144226
 
3.7%
m 144226
 
3.7%
Other values (7) 576904
14.8%
Uppercase Letter
ValueCountFrequency (%)
D 144226
33.3%
P 144226
33.3%
R 144225
33.3%
N 1
 
< 0.1%
Y 1
 
< 0.1%
C 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
576904
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4326777
88.2%
Common 576904
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 576904
13.3%
a 576901
13.3%
t 432678
10.0%
r 432677
10.0%
n 432676
10.0%
o 288452
 
6.7%
i 144227
 
3.3%
D 144226
 
3.3%
P 144226
 
3.3%
c 144226
 
3.3%
Other values (13) 1009584
23.3%
Common
ValueCountFrequency (%)
576904
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4903681
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
576904
11.8%
e 576904
11.8%
a 576901
11.8%
t 432678
 
8.8%
r 432677
 
8.8%
n 432676
 
8.8%
o 288452
 
5.9%
i 144227
 
2.9%
D 144226
 
2.9%
P 144226
 
2.9%
Other values (14) 1153810
23.5%

Complaint Type
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
New Tree Request
144226 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters2307616
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew Tree Request
2nd rowNew Tree Request
3rd rowNew Tree Request
4th rowNew Tree Request
5th rowNew Tree Request

Common Values

ValueCountFrequency (%)
New Tree Request 144226
100.0%

Length

2023-11-28T12:03:50.327387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T12:03:50.408743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
new 144226
33.3%
tree 144226
33.3%
request 144226
33.3%

Most occurring characters

ValueCountFrequency (%)
e 721130
31.2%
288452
 
12.5%
N 144226
 
6.2%
w 144226
 
6.2%
T 144226
 
6.2%
r 144226
 
6.2%
R 144226
 
6.2%
q 144226
 
6.2%
u 144226
 
6.2%
s 144226
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1586486
68.8%
Uppercase Letter 432678
 
18.8%
Space Separator 288452
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 721130
45.5%
w 144226
 
9.1%
r 144226
 
9.1%
q 144226
 
9.1%
u 144226
 
9.1%
s 144226
 
9.1%
t 144226
 
9.1%
Uppercase Letter
ValueCountFrequency (%)
N 144226
33.3%
T 144226
33.3%
R 144226
33.3%
Space Separator
ValueCountFrequency (%)
288452
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2019164
87.5%
Common 288452
 
12.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 721130
35.7%
N 144226
 
7.1%
w 144226
 
7.1%
T 144226
 
7.1%
r 144226
 
7.1%
R 144226
 
7.1%
q 144226
 
7.1%
u 144226
 
7.1%
s 144226
 
7.1%
t 144226
 
7.1%
Common
ValueCountFrequency (%)
288452
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2307616
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 721130
31.2%
288452
 
12.5%
N 144226
 
6.2%
w 144226
 
6.2%
T 144226
 
6.2%
r 144226
 
6.2%
R 144226
 
6.2%
q 144226
 
6.2%
u 144226
 
6.2%
s 144226
 
6.2%

Descriptor
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
For One Address
144226 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters2163390
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFor One Address
2nd rowFor One Address
3rd rowFor One Address
4th rowFor One Address
5th rowFor One Address

Common Values

ValueCountFrequency (%)
For One Address 144226
100.0%

Length

2023-11-28T12:03:50.476758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T12:03:50.557916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
for 144226
33.3%
one 144226
33.3%
address 144226
33.3%

Most occurring characters

ValueCountFrequency (%)
r 288452
13.3%
288452
13.3%
e 288452
13.3%
d 288452
13.3%
s 288452
13.3%
F 144226
6.7%
o 144226
6.7%
O 144226
6.7%
n 144226
6.7%
A 144226
6.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1442260
66.7%
Uppercase Letter 432678
 
20.0%
Space Separator 288452
 
13.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 288452
20.0%
e 288452
20.0%
d 288452
20.0%
s 288452
20.0%
o 144226
10.0%
n 144226
10.0%
Uppercase Letter
ValueCountFrequency (%)
F 144226
33.3%
O 144226
33.3%
A 144226
33.3%
Space Separator
ValueCountFrequency (%)
288452
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1874938
86.7%
Common 288452
 
13.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 288452
15.4%
e 288452
15.4%
d 288452
15.4%
s 288452
15.4%
F 144226
7.7%
o 144226
7.7%
O 144226
7.7%
n 144226
7.7%
A 144226
7.7%
Common
ValueCountFrequency (%)
288452
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2163390
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 288452
13.3%
288452
13.3%
e 288452
13.3%
d 288452
13.3%
s 288452
13.3%
F 144226
6.7%
o 144226
6.7%
O 144226
6.7%
n 144226
6.7%
A 144226
6.7%

location_type
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing10523
Missing (%)7.3%
Memory size1.1 MiB
Street
131034 
Park
 
1568
Street/Curbside
 
1101

Length

Max length15
Median length6
Mean length6.0506571
Min length4

Characters and Unicode

Total characters808991
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStreet
2nd rowStreet
3rd rowStreet
4th rowStreet
5th rowStreet

Common Values

ValueCountFrequency (%)
Street 131034
90.9%
Park 1568
 
1.1%
Street/Curbside 1101
 
0.8%
(Missing) 10523
 
7.3%

Length

2023-11-28T12:03:50.627265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T12:03:50.709616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
street 131034
98.0%
park 1568
 
1.2%
street/curbside 1101
 
0.8%

Most occurring characters

ValueCountFrequency (%)
e 265371
32.8%
t 264270
32.7%
r 134804
16.7%
S 132135
16.3%
P 1568
 
0.2%
a 1568
 
0.2%
k 1568
 
0.2%
/ 1101
 
0.1%
C 1101
 
0.1%
u 1101
 
0.1%
Other values (4) 4404
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 673086
83.2%
Uppercase Letter 134804
 
16.7%
Other Punctuation 1101
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 265371
39.4%
t 264270
39.3%
r 134804
20.0%
a 1568
 
0.2%
k 1568
 
0.2%
u 1101
 
0.2%
b 1101
 
0.2%
s 1101
 
0.2%
i 1101
 
0.2%
d 1101
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
S 132135
98.0%
P 1568
 
1.2%
C 1101
 
0.8%
Other Punctuation
ValueCountFrequency (%)
/ 1101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 807890
99.9%
Common 1101
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 265371
32.8%
t 264270
32.7%
r 134804
16.7%
S 132135
16.4%
P 1568
 
0.2%
a 1568
 
0.2%
k 1568
 
0.2%
C 1101
 
0.1%
u 1101
 
0.1%
b 1101
 
0.1%
Other values (3) 3303
 
0.4%
Common
ValueCountFrequency (%)
/ 1101
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 808991
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 265371
32.8%
t 264270
32.7%
r 134804
16.7%
S 132135
16.3%
P 1568
 
0.2%
a 1568
 
0.2%
k 1568
 
0.2%
/ 1101
 
0.1%
C 1101
 
0.1%
u 1101
 
0.1%
Other values (4) 4404
 
0.5%

incident_zip
Real number (ℝ)

HIGH CORRELATION 

Distinct205
Distinct (%)0.1%
Missing1240
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean10877.806
Minimum83
Maximum12345
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-11-28T12:03:50.786658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum83
5-th percentile10012
Q110308
median11215
Q311235
95-th percentile11418
Maximum12345
Range12262
Interquartile range (IQR)927

Descriptive statistics

Standard deviation547.32809
Coefficient of variation (CV)0.050316036
Kurtosis0.73882802
Mean10877.806
Median Absolute Deviation (MAD)158
Skewness-0.71616596
Sum1.555374 × 109
Variance299568.03
MonotonicityNot monotonic
2023-11-28T12:03:50.883732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11229 5274
 
3.7%
11235 4419
 
3.1%
11215 3913
 
2.7%
11385 3586
 
2.5%
11238 2668
 
1.8%
10467 2648
 
1.8%
11230 2417
 
1.7%
10021 1956
 
1.4%
10312 1931
 
1.3%
11218 1846
 
1.3%
Other values (195) 112328
77.9%
ValueCountFrequency (%)
83 2
 
< 0.1%
10000 1
 
< 0.1%
10001 512
 
0.4%
10002 887
0.6%
10003 1731
1.2%
10004 23
 
< 0.1%
10005 31
 
< 0.1%
10006 61
 
< 0.1%
10007 142
 
0.1%
10009 1238
0.9%
ValueCountFrequency (%)
12345 1
 
< 0.1%
11697 10
 
< 0.1%
11695 1
 
< 0.1%
11694 1024
0.7%
11693 237
 
0.2%
11692 89
 
0.1%
11691 309
 
0.2%
11436 248
 
0.2%
11435 406
 
0.3%
11434 358
 
0.2%

Incident Address
Text

MISSING 

Distinct91627
Distinct (%)64.7%
Missing2601
Missing (%)1.8%
Memory size1.1 MiB
2023-11-28T12:03:51.088203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length64
Median length59
Mean length17.733515
Min length2

Characters and Unicode

Total characters2511509
Distinct characters67
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique67046 ?
Unique (%)47.3%

Sample

1st row1907 1 AVENUE
2nd row526 WEST 146 STREET
3rd row2252 1 AVENUE
4th row2252 1 AVENUE
5th row250 UNION AVENUE
ValueCountFrequency (%)
street 69627
 
15.1%
avenue 46825
 
10.1%
east 19114
 
4.1%
west 10235
 
2.2%
road 5823
 
1.3%
boulevard 5578
 
1.2%
place 4689
 
1.0%
st 2064
 
0.4%
park 1548
 
0.3%
parkway 1494
 
0.3%
Other values (18636) 295245
63.9%
2023-11-28T12:03:51.416474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
350414
14.0%
E 331669
 
13.2%
T 208880
 
8.3%
A 134853
 
5.4%
R 134252
 
5.3%
S 130487
 
5.2%
1 117112
 
4.7%
N 99658
 
4.0%
2 83287
 
3.3%
3 67681
 
2.7%
Other values (57) 853216
34.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1516055
60.4%
Decimal Number 612465
24.4%
Space Separator 350414
 
14.0%
Dash Punctuation 30805
 
1.2%
Lowercase Letter 1651
 
0.1%
Other Punctuation 119
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 331669
21.9%
T 208880
13.8%
A 134853
8.9%
R 134252
8.9%
S 130487
 
8.6%
N 99658
 
6.6%
U 64904
 
4.3%
V 60019
 
4.0%
O 57404
 
3.8%
L 41856
 
2.8%
Other values (16) 252073
16.6%
Lowercase Letter
ValueCountFrequency (%)
a 218
13.2%
r 204
12.4%
e 159
9.6%
o 130
 
7.9%
n 130
 
7.9%
l 105
 
6.4%
s 93
 
5.6%
i 88
 
5.3%
k 85
 
5.1%
t 69
 
4.2%
Other values (15) 370
22.4%
Decimal Number
ValueCountFrequency (%)
1 117112
19.1%
2 83287
13.6%
3 67681
11.1%
0 57008
9.3%
5 56735
9.3%
4 56073
9.2%
6 46873
7.7%
7 45400
 
7.4%
8 44744
 
7.3%
9 37552
 
6.1%
Other Punctuation
ValueCountFrequency (%)
' 80
67.2%
/ 19
 
16.0%
. 19
 
16.0%
& 1
 
0.8%
Space Separator
ValueCountFrequency (%)
350414
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 30805
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1517706
60.4%
Common 993803
39.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 331669
21.9%
T 208880
13.8%
A 134853
8.9%
R 134252
8.8%
S 130487
 
8.6%
N 99658
 
6.6%
U 64904
 
4.3%
V 60019
 
4.0%
O 57404
 
3.8%
L 41856
 
2.8%
Other values (41) 253724
16.7%
Common
ValueCountFrequency (%)
350414
35.3%
1 117112
 
11.8%
2 83287
 
8.4%
3 67681
 
6.8%
0 57008
 
5.7%
5 56735
 
5.7%
4 56073
 
5.6%
6 46873
 
4.7%
7 45400
 
4.6%
8 44744
 
4.5%
Other values (6) 68476
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2511509
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
350414
14.0%
E 331669
 
13.2%
T 208880
 
8.3%
A 134853
 
5.4%
R 134252
 
5.3%
S 130487
 
5.2%
1 117112
 
4.7%
N 99658
 
4.0%
2 83287
 
3.3%
3 67681
 
2.7%
Other values (57) 853216
34.0%

Street Name
Text

MISSING 

Distinct7366
Distinct (%)5.2%
Missing2601
Missing (%)1.8%
Memory size1.1 MiB
2023-11-28T12:03:51.621023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length42
Median length31
Mean length13.123269
Min length2

Characters and Unicode

Total characters1858583
Distinct characters40
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1893 ?
Unique (%)1.3%

Sample

1st row1 AVENUE
2nd rowWEST 146 STREET
3rd row1 AVENUE
4th row1 AVENUE
5th rowUNION AVENUE
ValueCountFrequency (%)
street 69627
21.6%
avenue 46825
 
14.5%
east 19110
 
5.9%
west 10235
 
3.2%
road 5823
 
1.8%
boulevard 5578
 
1.7%
place 4689
 
1.5%
st 2060
 
0.6%
parkway 1494
 
0.5%
park 1468
 
0.5%
Other values (3528) 155157
48.2%
2023-11-28T12:03:51.934606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 331637
17.8%
210238
11.3%
T 208861
11.2%
A 134369
 
7.2%
R 134245
 
7.2%
S 130471
 
7.0%
N 99654
 
5.4%
U 64904
 
3.5%
V 60015
 
3.2%
O 57402
 
3.1%
Other values (30) 426787
23.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1515098
81.5%
Space Separator 210238
 
11.3%
Decimal Number 133156
 
7.2%
Other Punctuation 86
 
< 0.1%
Dash Punctuation 5
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 331637
21.9%
T 208861
13.8%
A 134369
8.9%
R 134245
8.9%
S 130471
 
8.6%
N 99654
 
6.6%
U 64904
 
4.3%
V 60015
 
4.0%
O 57402
 
3.8%
L 41846
 
2.8%
Other values (16) 251694
16.6%
Decimal Number
ValueCountFrequency (%)
1 28446
21.4%
2 18420
13.8%
3 13355
10.0%
7 12512
9.4%
4 11726
8.8%
8 11491
8.6%
5 10890
 
8.2%
6 10161
 
7.6%
9 9396
 
7.1%
0 6759
 
5.1%
Other Punctuation
ValueCountFrequency (%)
' 80
93.0%
. 6
 
7.0%
Space Separator
ValueCountFrequency (%)
210238
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1515098
81.5%
Common 343485
 
18.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 331637
21.9%
T 208861
13.8%
A 134369
8.9%
R 134245
8.9%
S 130471
 
8.6%
N 99654
 
6.6%
U 64904
 
4.3%
V 60015
 
4.0%
O 57402
 
3.8%
L 41846
 
2.8%
Other values (16) 251694
16.6%
Common
ValueCountFrequency (%)
210238
61.2%
1 28446
 
8.3%
2 18420
 
5.4%
3 13355
 
3.9%
7 12512
 
3.6%
4 11726
 
3.4%
8 11491
 
3.3%
5 10890
 
3.2%
6 10161
 
3.0%
9 9396
 
2.7%
Other values (4) 6850
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1858583
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 331637
17.8%
210238
11.3%
T 208861
11.2%
A 134369
 
7.2%
R 134245
 
7.2%
S 130471
 
7.0%
N 99654
 
5.4%
U 64904
 
3.5%
V 60015
 
3.2%
O 57402
 
3.1%
Other values (30) 426787
23.0%

Cross Street 1
Text

MISSING 

Distinct6340
Distinct (%)5.0%
Missing16781
Missing (%)11.6%
Memory size1.1 MiB
2023-11-28T12:03:52.146822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length32
Median length29
Mean length12.602205
Min length4

Characters and Unicode

Total characters1606088
Distinct characters42
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1386 ?
Unique (%)1.1%

Sample

1st rowEAST 97 STREET
2nd rowAMSTERDAM AVENUE
3rd rowEAST 115 STREET
4th rowEAST 115 STREET
5th rowMESEROLE STREET
ValueCountFrequency (%)
avenue 62819
22.2%
street 40254
 
14.2%
east 10596
 
3.7%
west 6166
 
2.2%
road 5391
 
1.9%
place 4309
 
1.5%
boulevard 3725
 
1.3%
5 2301
 
0.8%
3 2116
 
0.7%
2 1685
 
0.6%
Other values (3806) 143208
50.7%
2023-11-28T12:03:52.462845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 289129
18.0%
173121
10.8%
A 138478
 
8.6%
T 130195
 
8.1%
N 112448
 
7.0%
R 98390
 
6.1%
S 85888
 
5.3%
U 79894
 
5.0%
V 73327
 
4.6%
O 51315
 
3.2%
Other values (32) 373903
23.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1329281
82.8%
Space Separator 173121
 
10.8%
Decimal Number 103435
 
6.4%
Dash Punctuation 171
 
< 0.1%
Other Punctuation 80
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 289129
21.8%
A 138478
10.4%
T 130195
9.8%
N 112448
 
8.5%
R 98390
 
7.4%
S 85888
 
6.5%
U 79894
 
6.0%
V 73327
 
5.5%
O 51315
 
3.9%
L 39383
 
3.0%
Other values (16) 230834
17.4%
Decimal Number
ValueCountFrequency (%)
1 22683
21.9%
2 13253
12.8%
3 10894
10.5%
5 9635
9.3%
8 9050
 
8.7%
4 8732
 
8.4%
6 8286
 
8.0%
7 7881
 
7.6%
9 6620
 
6.4%
0 6401
 
6.2%
Other Punctuation
ValueCountFrequency (%)
' 46
57.5%
/ 28
35.0%
& 5
 
6.2%
. 1
 
1.2%
Space Separator
ValueCountFrequency (%)
173121
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 171
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1329281
82.8%
Common 276807
 
17.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 289129
21.8%
A 138478
10.4%
T 130195
9.8%
N 112448
 
8.5%
R 98390
 
7.4%
S 85888
 
6.5%
U 79894
 
6.0%
V 73327
 
5.5%
O 51315
 
3.9%
L 39383
 
3.0%
Other values (16) 230834
17.4%
Common
ValueCountFrequency (%)
173121
62.5%
1 22683
 
8.2%
2 13253
 
4.8%
3 10894
 
3.9%
5 9635
 
3.5%
8 9050
 
3.3%
4 8732
 
3.2%
6 8286
 
3.0%
7 7881
 
2.8%
9 6620
 
2.4%
Other values (6) 6652
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1606088
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 289129
18.0%
173121
10.8%
A 138478
 
8.6%
T 130195
 
8.1%
N 112448
 
7.0%
R 98390
 
6.1%
S 85888
 
5.3%
U 79894
 
5.0%
V 73327
 
4.6%
O 51315
 
3.2%
Other values (32) 373903
23.3%

Cross Street 2
Text

MISSING 

Distinct6423
Distinct (%)5.0%
Missing15910
Missing (%)11.0%
Memory size1.1 MiB
2023-11-28T12:03:52.642401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length32
Median length29
Mean length12.748636
Min length4

Characters and Unicode

Total characters1635854
Distinct characters43
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1418 ?
Unique (%)1.1%

Sample

1st rowEAST 99 STREET
2nd rowBROADWAY
3rd rowEAST 116 STREET
4th rowEAST 116 STREET
5th rowSCHOLES STREET
ValueCountFrequency (%)
avenue 62508
21.8%
street 41344
 
14.4%
east 11889
 
4.1%
west 6074
 
2.1%
road 4891
 
1.7%
place 4444
 
1.5%
boulevard 3719
 
1.3%
3 1546
 
0.5%
drive 1520
 
0.5%
1 1492
 
0.5%
Other values (3835) 147433
51.4%
2023-11-28T12:03:52.923027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 294192
18.0%
177891
10.9%
A 139272
 
8.5%
T 134679
 
8.2%
N 111684
 
6.8%
R 102324
 
6.3%
S 88330
 
5.4%
U 79508
 
4.9%
V 74391
 
4.5%
O 52288
 
3.2%
Other values (33) 381295
23.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1350960
82.6%
Space Separator 177891
 
10.9%
Decimal Number 106762
 
6.5%
Dash Punctuation 124
 
< 0.1%
Other Punctuation 115
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 294192
21.8%
A 139272
10.3%
T 134679
10.0%
N 111684
 
8.3%
R 102324
 
7.6%
S 88330
 
6.5%
U 79508
 
5.9%
V 74391
 
5.5%
O 52288
 
3.9%
L 39105
 
2.9%
Other values (16) 235187
17.4%
Decimal Number
ValueCountFrequency (%)
1 23883
22.4%
2 17127
16.0%
3 10546
9.9%
4 8650
 
8.1%
8 8522
 
8.0%
6 8474
 
7.9%
5 8304
 
7.8%
7 8214
 
7.7%
9 6790
 
6.4%
0 6252
 
5.9%
Other Punctuation
ValueCountFrequency (%)
/ 83
72.2%
' 30
 
26.1%
& 2
 
1.7%
Space Separator
ValueCountFrequency (%)
177891
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 124
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1350960
82.6%
Common 284894
 
17.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 294192
21.8%
A 139272
10.3%
T 134679
10.0%
N 111684
 
8.3%
R 102324
 
7.6%
S 88330
 
6.5%
U 79508
 
5.9%
V 74391
 
5.5%
O 52288
 
3.9%
L 39105
 
2.9%
Other values (16) 235187
17.4%
Common
ValueCountFrequency (%)
177891
62.4%
1 23883
 
8.4%
2 17127
 
6.0%
3 10546
 
3.7%
4 8650
 
3.0%
8 8522
 
3.0%
6 8474
 
3.0%
5 8304
 
2.9%
7 8214
 
2.9%
9 6790
 
2.4%
Other values (7) 6493
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1635854
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 294192
18.0%
177891
10.9%
A 139272
 
8.5%
T 134679
 
8.2%
N 111684
 
6.8%
R 102324
 
6.3%
S 88330
 
5.4%
U 79508
 
4.9%
V 74391
 
4.5%
O 52288
 
3.2%
Other values (33) 381295
23.3%

Intersection Street 1
Text

MISSING 

Distinct5708
Distinct (%)6.3%
Missing53208
Missing (%)36.9%
Memory size1.1 MiB
2023-11-28T12:03:53.099048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length32
Median length29
Mean length12.78034
Min length4

Characters and Unicode

Total characters1163241
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1406 ?
Unique (%)1.5%

Sample

1st rowEAST 97 STREET
2nd rowAMSTERDAM AVENUE
3rd rowEAST 115 STREET
4th rowEAST 115 STREET
5th rowMESEROLE STREET
ValueCountFrequency (%)
avenue 43808
21.6%
street 29516
 
14.5%
east 7243
 
3.6%
west 4990
 
2.5%
road 3869
 
1.9%
place 3122
 
1.5%
boulevard 2515
 
1.2%
5 1623
 
0.8%
3 1525
 
0.8%
2 1184
 
0.6%
Other values (3581) 103712
51.1%
2023-11-28T12:03:53.375730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 206990
17.8%
130390
11.2%
A 98847
 
8.5%
T 95800
 
8.2%
N 80370
 
6.9%
R 71588
 
6.2%
S 62978
 
5.4%
U 56126
 
4.8%
V 51120
 
4.4%
O 37469
 
3.2%
Other values (31) 271563
23.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 958826
82.4%
Space Separator 130390
 
11.2%
Decimal Number 73789
 
6.3%
Dash Punctuation 169
 
< 0.1%
Other Punctuation 67
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 206990
21.6%
A 98847
10.3%
T 95800
10.0%
N 80370
 
8.4%
R 71588
 
7.5%
S 62978
 
6.6%
U 56126
 
5.9%
V 51120
 
5.3%
O 37469
 
3.9%
L 28693
 
3.0%
Other values (16) 168845
17.6%
Decimal Number
ValueCountFrequency (%)
1 16495
22.4%
2 9716
13.2%
3 7626
10.3%
5 6853
9.3%
8 6458
 
8.8%
4 6317
 
8.6%
6 5790
 
7.8%
7 5341
 
7.2%
0 4638
 
6.3%
9 4555
 
6.2%
Other Punctuation
ValueCountFrequency (%)
' 37
55.2%
/ 25
37.3%
& 5
 
7.5%
Space Separator
ValueCountFrequency (%)
130390
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 169
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 958826
82.4%
Common 204415
 
17.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 206990
21.6%
A 98847
10.3%
T 95800
10.0%
N 80370
 
8.4%
R 71588
 
7.5%
S 62978
 
6.6%
U 56126
 
5.9%
V 51120
 
5.3%
O 37469
 
3.9%
L 28693
 
3.0%
Other values (16) 168845
17.6%
Common
ValueCountFrequency (%)
130390
63.8%
1 16495
 
8.1%
2 9716
 
4.8%
3 7626
 
3.7%
5 6853
 
3.4%
8 6458
 
3.2%
4 6317
 
3.1%
6 5790
 
2.8%
7 5341
 
2.6%
0 4638
 
2.3%
Other values (5) 4791
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1163241
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 206990
17.8%
130390
11.2%
A 98847
 
8.5%
T 95800
 
8.2%
N 80370
 
6.9%
R 71588
 
6.2%
S 62978
 
5.4%
U 56126
 
4.8%
V 51120
 
4.4%
O 37469
 
3.2%
Other values (31) 271563
23.3%

Intersection Street 2
Text

MISSING 

Distinct5823
Distinct (%)6.3%
Missing52337
Missing (%)36.3%
Memory size1.1 MiB
2023-11-28T12:03:53.585735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length32
Median length29
Mean length12.982381
Min length4

Characters and Unicode

Total characters1192938
Distinct characters44
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1489 ?
Unique (%)1.6%

Sample

1st rowEAST 99 STREET
2nd rowBROADWAY
3rd rowEAST 116 STREET
4th rowEAST 116 STREET
5th rowSCHOLES STREET
ValueCountFrequency (%)
avenue 43764
21.1%
street 30367
 
14.7%
east 8306
 
4.0%
west 4921
 
2.4%
road 3406
 
1.6%
place 3214
 
1.6%
boulevard 2679
 
1.3%
222 1283
 
0.6%
drive 1159
 
0.6%
beach 1073
 
0.5%
Other values (3621) 106853
51.6%
2023-11-28T12:03:53.895162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 212274
17.8%
134650
11.3%
A 100350
 
8.4%
T 99295
 
8.3%
N 80006
 
6.7%
R 75618
 
6.3%
S 64943
 
5.4%
U 56148
 
4.7%
V 52415
 
4.4%
O 37887
 
3.2%
Other values (34) 279352
23.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 981452
82.3%
Space Separator 134650
 
11.3%
Decimal Number 76601
 
6.4%
Dash Punctuation 123
 
< 0.1%
Other Punctuation 110
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 212274
21.6%
A 100350
10.2%
T 99295
10.1%
N 80006
 
8.2%
R 75618
 
7.7%
S 64943
 
6.6%
U 56148
 
5.7%
V 52415
 
5.3%
O 37887
 
3.9%
L 28935
 
2.9%
Other values (16) 173581
17.7%
Decimal Number
ValueCountFrequency (%)
1 17131
22.4%
2 13431
17.5%
3 7380
9.6%
4 6227
 
8.1%
6 5986
 
7.8%
5 5965
 
7.8%
8 5921
 
7.7%
7 5574
 
7.3%
9 4676
 
6.1%
0 4310
 
5.6%
Other Punctuation
ValueCountFrequency (%)
/ 81
73.6%
' 26
 
23.6%
& 2
 
1.8%
. 1
 
0.9%
Space Separator
ValueCountFrequency (%)
134650
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 123
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 981452
82.3%
Common 211486
 
17.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 212274
21.6%
A 100350
10.2%
T 99295
10.1%
N 80006
 
8.2%
R 75618
 
7.7%
S 64943
 
6.6%
U 56148
 
5.7%
V 52415
 
5.3%
O 37887
 
3.9%
L 28935
 
2.9%
Other values (16) 173581
17.7%
Common
ValueCountFrequency (%)
134650
63.7%
1 17131
 
8.1%
2 13431
 
6.4%
3 7380
 
3.5%
4 6227
 
2.9%
6 5986
 
2.8%
5 5965
 
2.8%
8 5921
 
2.8%
7 5574
 
2.6%
9 4676
 
2.2%
Other values (8) 4545
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1192938
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 212274
17.8%
134650
11.3%
A 100350
 
8.4%
T 99295
 
8.3%
N 80006
 
6.7%
R 75618
 
6.3%
S 64943
 
5.4%
U 56148
 
4.7%
V 52415
 
4.4%
O 37887
 
3.2%
Other values (34) 279352
23.4%

Address Type
Categorical

IMBALANCE  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing15612
Missing (%)10.8%
Memory size1.1 MiB
ADDRESS
112640 
LATLONG
13497 
INTERSECTION
 
1723
BLOCKFACE
 
517
UNRECOGNIZED
 
236

Length

Max length12
Median length7
Mean length7.0842132
Min length7

Characters and Unicode

Total characters911129
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowADDRESS
2nd rowADDRESS
3rd rowADDRESS
4th rowADDRESS
5th rowADDRESS

Common Values

ValueCountFrequency (%)
ADDRESS 112640
78.1%
LATLONG 13497
 
9.4%
INTERSECTION 1723
 
1.2%
BLOCKFACE 517
 
0.4%
UNRECOGNIZED 236
 
0.2%
PLACENAME 1
 
< 0.1%
(Missing) 15612
 
10.8%

Length

2023-11-28T12:03:54.001743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T12:03:54.094381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
address 112640
87.6%
latlong 13497
 
10.5%
intersection 1723
 
1.3%
blockface 517
 
0.4%
unrecognized 236
 
0.2%
placename 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
S 227003
24.9%
D 225516
24.8%
A 126656
13.9%
E 117077
12.8%
R 114599
12.6%
L 27512
 
3.0%
N 17416
 
1.9%
T 16943
 
1.9%
O 15973
 
1.8%
G 13733
 
1.5%
Other values (9) 8701
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 911129
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 227003
24.9%
D 225516
24.8%
A 126656
13.9%
E 117077
12.8%
R 114599
12.6%
L 27512
 
3.0%
N 17416
 
1.9%
T 16943
 
1.9%
O 15973
 
1.8%
G 13733
 
1.5%
Other values (9) 8701
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 911129
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 227003
24.9%
D 225516
24.8%
A 126656
13.9%
E 117077
12.8%
R 114599
12.6%
L 27512
 
3.0%
N 17416
 
1.9%
T 16943
 
1.9%
O 15973
 
1.8%
G 13733
 
1.5%
Other values (9) 8701
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 911129
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 227003
24.9%
D 225516
24.8%
A 126656
13.9%
E 117077
12.8%
R 114599
12.6%
L 27512
 
3.0%
N 17416
 
1.9%
T 16943
 
1.9%
O 15973
 
1.8%
G 13733
 
1.5%
Other values (9) 8701
 
1.0%

City
Categorical

HIGH CORRELATION  MISSING 

Distinct50
Distinct (%)< 0.1%
Missing2297
Missing (%)1.6%
Memory size1.1 MiB
BROOKLYN
56534 
NEW YORK
30173 
BRONX
11611 
STATEN ISLAND
9720 
RIDGEWOOD
 
3566
Other values (45)
30325 

Length

Max length19
Median length8
Mean length8.6705043
Min length2

Characters and Unicode

Total characters1230596
Distinct characters34
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowNEW YORK
2nd rowNEW YORK
3rd rowNEW YORK
4th rowNEW YORK
5th rowBROOKLYN

Common Values

ValueCountFrequency (%)
BROOKLYN 56534
39.2%
NEW YORK 30173
20.9%
BRONX 11611
 
8.1%
STATEN ISLAND 9720
 
6.7%
RIDGEWOOD 3566
 
2.5%
ASTORIA 2607
 
1.8%
FLUSHING 2420
 
1.7%
JAMAICA 2013
 
1.4%
JACKSON HEIGHTS 1382
 
1.0%
HOWARD BEACH 1349
 
0.9%
Other values (40) 20554
 
14.3%
(Missing) 2297
 
1.6%

Length

2023-11-28T12:03:54.185834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
brooklyn 56534
28.3%
new 30221
15.1%
york 30173
15.1%
bronx 11611
 
5.8%
island 10465
 
5.2%
staten 9720
 
4.9%
ridgewood 3566
 
1.8%
astoria 2607
 
1.3%
flushing 2420
 
1.2%
park 2244
 
1.1%
Other values (52) 40063
20.1%

Most occurring characters

ValueCountFrequency (%)
O 184625
15.0%
N 134104
10.9%
R 120468
9.8%
K 94127
 
7.6%
Y 90649
 
7.4%
L 86846
 
7.1%
B 71726
 
5.8%
E 69050
 
5.6%
57695
 
4.7%
A 53477
 
4.3%
Other values (24) 267829
21.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1172891
95.3%
Space Separator 57695
 
4.7%
Lowercase Letter 10
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 184625
15.7%
N 134104
11.4%
R 120468
10.3%
K 94127
8.0%
Y 90649
7.7%
L 86846
 
7.4%
B 71726
 
6.1%
E 69050
 
5.9%
A 53477
 
4.6%
S 44442
 
3.8%
Other values (16) 223377
19.0%
Lowercase Letter
ValueCountFrequency (%)
a 3
30.0%
k 2
20.0%
o 1
 
10.0%
c 1
 
10.0%
w 1
 
10.0%
y 1
 
10.0%
r 1
 
10.0%
Space Separator
ValueCountFrequency (%)
57695
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1172901
95.3%
Common 57695
 
4.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 184625
15.7%
N 134104
11.4%
R 120468
10.3%
K 94127
8.0%
Y 90649
7.7%
L 86846
 
7.4%
B 71726
 
6.1%
E 69050
 
5.9%
A 53477
 
4.6%
S 44442
 
3.8%
Other values (23) 223387
19.0%
Common
ValueCountFrequency (%)
57695
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1230596
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 184625
15.0%
N 134104
10.9%
R 120468
9.8%
K 94127
 
7.6%
Y 90649
 
7.4%
L 86846
 
7.1%
B 71726
 
5.8%
E 69050
 
5.6%
57695
 
4.7%
A 53477
 
4.3%
Other values (24) 267829
21.8%

Landmark
Text

MISSING 

Distinct4402
Distinct (%)5.8%
Missing68746
Missing (%)47.7%
Memory size1.1 MiB
2023-11-28T12:03:54.365708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length32
Median length29
Mean length13.32651
Min length6

Characters and Unicode

Total characters1005885
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1003 ?
Unique (%)1.3%

Sample

1st row1 AVENUE
2nd rowWEST 146 STREET
3rd row1 AVENUE
4th row1 AVENUE
5th rowUNION AVENUE
ValueCountFrequency (%)
street 36865
21.5%
avenue 25436
 
14.8%
east 9285
 
5.4%
west 5957
 
3.5%
boulevard 3491
 
2.0%
road 3414
 
2.0%
place 2405
 
1.4%
bronx 1406
 
0.8%
3 912
 
0.5%
parkway 879
 
0.5%
Other values (2754) 81736
47.6%
2023-11-28T12:03:54.761418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 176976
17.6%
126849
12.6%
T 108105
10.7%
R 72303
 
7.2%
A 71914
 
7.1%
S 67760
 
6.7%
N 54180
 
5.4%
U 35407
 
3.5%
V 32596
 
3.2%
O 32132
 
3.2%
Other values (31) 227663
22.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 808021
80.3%
Space Separator 126849
 
12.6%
Decimal Number 71004
 
7.1%
Other Punctuation 8
 
< 0.1%
Dash Punctuation 3
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 176976
21.9%
T 108105
13.4%
R 72303
8.9%
A 71914
8.9%
S 67760
 
8.4%
N 54180
 
6.7%
U 35407
 
4.4%
V 32596
 
4.0%
O 32132
 
4.0%
L 22744
 
2.8%
Other values (16) 133904
16.6%
Decimal Number
ValueCountFrequency (%)
1 15219
21.4%
2 9594
13.5%
3 7051
9.9%
7 6563
9.2%
5 6391
9.0%
4 6235
8.8%
8 6073
 
8.6%
6 5389
 
7.6%
9 4892
 
6.9%
0 3597
 
5.1%
Other Punctuation
ValueCountFrequency (%)
' 4
50.0%
& 3
37.5%
/ 1
 
12.5%
Space Separator
ValueCountFrequency (%)
126849
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 808021
80.3%
Common 197864
 
19.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 176976
21.9%
T 108105
13.4%
R 72303
8.9%
A 71914
8.9%
S 67760
 
8.4%
N 54180
 
6.7%
U 35407
 
4.4%
V 32596
 
4.0%
O 32132
 
4.0%
L 22744
 
2.8%
Other values (16) 133904
16.6%
Common
ValueCountFrequency (%)
126849
64.1%
1 15219
 
7.7%
2 9594
 
4.8%
3 7051
 
3.6%
7 6563
 
3.3%
5 6391
 
3.2%
4 6235
 
3.2%
8 6073
 
3.1%
6 5389
 
2.7%
9 4892
 
2.5%
Other values (5) 3608
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1005885
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 176976
17.6%
126849
12.6%
T 108105
10.7%
R 72303
 
7.2%
A 71914
 
7.1%
S 67760
 
6.7%
N 54180
 
5.4%
U 35407
 
3.5%
V 32596
 
3.2%
O 32132
 
3.2%
Other values (31) 227663
22.6%

Facility Type
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing144225
Missing (%)> 99.9%
Memory size1.1 MiB
2023-11-28T12:03:54.853646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters8
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowPrecinct
ValueCountFrequency (%)
precinct 1
100.0%
2023-11-28T12:03:55.008377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 2
25.0%
P 1
12.5%
r 1
12.5%
e 1
12.5%
i 1
12.5%
n 1
12.5%
t 1
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7
87.5%
Uppercase Letter 1
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 2
28.6%
r 1
14.3%
e 1
14.3%
i 1
14.3%
n 1
14.3%
t 1
14.3%
Uppercase Letter
ValueCountFrequency (%)
P 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 2
25.0%
P 1
12.5%
r 1
12.5%
e 1
12.5%
i 1
12.5%
n 1
12.5%
t 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 2
25.0%
P 1
12.5%
r 1
12.5%
e 1
12.5%
i 1
12.5%
n 1
12.5%
t 1
12.5%

Status
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Closed
66553 
In Progress
56477 
Assigned
15126 
Open
 
6057
Unspecified
 
10
Other values (2)
 
3

Length

Max length16
Median length11
Mean length8.0841735
Min length4

Characters and Unicode

Total characters1165948
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowIn Progress
2nd rowIn Progress
3rd rowIn Progress
4th rowIn Progress
5th rowIn Progress

Common Values

ValueCountFrequency (%)
Closed 66553
46.1%
In Progress 56477
39.2%
Assigned 15126
 
10.5%
Open 6057
 
4.2%
Unspecified 10
 
< 0.1%
Closed - Testing 2
 
< 0.1%
Draft 1
 
< 0.1%

Length

2023-11-28T12:03:55.103232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T12:03:55.203886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
closed 66555
33.2%
in 56477
28.1%
progress 56477
28.1%
assigned 15126
 
7.5%
open 6057
 
3.0%
unspecified 10
 
< 0.1%
2
 
< 0.1%
testing 2
 
< 0.1%
draft 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
s 209773
18.0%
e 144237
12.4%
o 123032
10.6%
r 112955
9.7%
d 81691
 
7.0%
n 77672
 
6.7%
g 71605
 
6.1%
C 66555
 
5.7%
l 66555
 
5.7%
56481
 
4.8%
Other values (14) 155392
13.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 908760
77.9%
Uppercase Letter 200705
 
17.2%
Space Separator 56481
 
4.8%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 209773
23.1%
e 144237
15.9%
o 123032
13.5%
r 112955
12.4%
d 81691
 
9.0%
n 77672
 
8.5%
g 71605
 
7.9%
l 66555
 
7.3%
i 15148
 
1.7%
p 6067
 
0.7%
Other values (4) 25
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
C 66555
33.2%
I 56477
28.1%
P 56477
28.1%
A 15126
 
7.5%
O 6057
 
3.0%
U 10
 
< 0.1%
T 2
 
< 0.1%
D 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
56481
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1109465
95.2%
Common 56483
 
4.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 209773
18.9%
e 144237
13.0%
o 123032
11.1%
r 112955
10.2%
d 81691
 
7.4%
n 77672
 
7.0%
g 71605
 
6.5%
C 66555
 
6.0%
l 66555
 
6.0%
I 56477
 
5.1%
Other values (12) 98913
8.9%
Common
ValueCountFrequency (%)
56481
> 99.9%
- 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1165948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 209773
18.0%
e 144237
12.4%
o 123032
10.6%
r 112955
9.7%
d 81691
 
7.0%
n 77672
 
6.7%
g 71605
 
6.1%
C 66555
 
5.7%
l 66555
 
5.7%
56481
 
4.8%
Other values (14) 155392
13.3%

Due Date
Date

MISSING 

Distinct65216
Distinct (%)98.5%
Missing78009
Missing (%)54.1%
Memory size1.1 MiB
Minimum2016-06-29 09:35:43
Maximum2021-06-17 19:02:46
2023-11-28T12:03:55.301456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:55.400331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Resolution Description
Categorical

HIGH CORRELATION  MISSING 

Distinct47
Distinct (%)< 0.1%
Missing10930
Missing (%)7.6%
Memory size1.1 MiB
The Department of Parks and Recreation has reviewed this request and will visit the location to investigate the condition.
20049 
The Department of Parks and Recreation has determined that the requested location cannot receive a new tree because of conflicts with surrounding infrastructure.
19825 
NYC Parks will inspect the site to determine if it is suitable for a new street tree, including a review of potential conflicts with other infrastructure. If the site is found to be suitable, a tree will be planted during the next available planting season. You may review recently completed and upcoming street tree plantings in your neighborhood by visiting the NYC Parks Tree Work Hub at nyc.gov/parks/treework.
19296 
NYC Parks determined that the requested location cannot receive a new tree because of conflicts with surrounding infrastructure.
18435 
NYC Parks reviewed this request and will visit the location to investigate the condition. Under NYC Parks’ Tree Risk Management Program, all trees under the agency's jurisdiction are assessed for risk, and work is prioritized to address the conditions with the highest risk first. For more information about the Tree Risk Management Program, visit the NYC Urban Forest page on the NYC Parks website at nyc.gov/parks/trees. To learn more about the trees in your neighborhood, visit the NYC Tree Map at nyc.gov/parks/treemap.
17131 
Other values (42)
38560 

Length

Max length529
Median length287
Mean length270.94182
Min length63

Characters and Unicode

Total characters36115461
Distinct characters57
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowNYC Parks determined that the requested location cannot receive a new tree because of conflicts with surrounding infrastructure.
2nd rowNYC Parks visited the location but could not find the tree condition you reported.
3rd rowNYC Parks determined that the requested location cannot receive a new tree because of conflicts with surrounding infrastructure.
4th rowThe location was inspected and is suitable for tree planting. Barring any unforeseen issues such as underground utilities and based on available funding, the requested tree will be placed on an upcoming contract. Please note that this process can take up to two years, depending on capacity and the location. To find out where and when trees will be planted, please visit the Tree Work Hub at nyc.gov/parks/treework. To learn more about the trees in your neighborhood, visit the NYC Street Tree Map at nyc.gov/parks/treemap.
5th rowThe location was inspected and is suitable for tree planting. Barring any unforeseen issues such as underground utilities and based on available funding, the requested tree will be placed on an upcoming contract. Please note that this process can take up to two years, depending on capacity and the location. To find out where and when trees will be planted, please visit the Tree Work Hub at nyc.gov/parks/treework. To learn more about the trees in your neighborhood, visit the NYC Street Tree Map at nyc.gov/parks/treemap.

Common Values

ValueCountFrequency (%)
The Department of Parks and Recreation has reviewed this request and will visit the location to investigate the condition. 20049
13.9%
The Department of Parks and Recreation has determined that the requested location cannot receive a new tree because of conflicts with surrounding infrastructure. 19825
13.7%
NYC Parks will inspect the site to determine if it is suitable for a new street tree, including a review of potential conflicts with other infrastructure. If the site is found to be suitable, a tree will be planted during the next available planting season. You may review recently completed and upcoming street tree plantings in your neighborhood by visiting the NYC Parks Tree Work Hub at nyc.gov/parks/treework. 19296
13.4%
NYC Parks determined that the requested location cannot receive a new tree because of conflicts with surrounding infrastructure. 18435
12.8%
NYC Parks reviewed this request and will visit the location to investigate the condition. Under NYC Parks’ Tree Risk Management Program, all trees under the agency's jurisdiction are assessed for risk, and work is prioritized to address the conditions with the highest risk first. For more information about the Tree Risk Management Program, visit the NYC Urban Forest page on the NYC Parks website at nyc.gov/parks/trees. To learn more about the trees in your neighborhood, visit the NYC Tree Map at nyc.gov/parks/treemap. 17131
11.9%
The location was inspected and is suitable for tree planting. Barring any unforeseen issues such as underground utilities and based on available funding, the requested tree will be placed on an upcoming contract. Please note that this process can take up to two years, depending on capacity and the location. To find out where and when trees will be planted, please visit the Tree Work Hub at nyc.gov/parks/treework. To learn more about the trees in your neighborhood, visit the NYC Street Tree Map at nyc.gov/parks/treemap. 10177
7.1%
The Department of Parks and Recreation will review your request within one year. It may take up to 24 months to inspect the location for planting. To learn more about the street tree planting process, visit www.nyc.gov/parks. Please note your Service Request number for future reference. 6030
 
4.2%
The Department of Parks and Recreation has planted the tree(s). 4195
 
2.9%
The Service Request submitted did not have sufficient location or complaint information for NYC Parks to respond. Please submit a new Service Request with adequate site details, including a full street address and descriptive location information. 3004
 
2.1%
The location has been inspected and is scheduled for planting during the spring planting season. The spring planting season runs from approximately March to May, weather permitting. 2746
 
1.9%
Other values (37) 12408
8.6%
(Missing) 10930
7.6%

Length

2023-11-28T12:03:55.515040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 496683
 
8.8%
tree 216275
 
3.8%
and 180957
 
3.2%
to 170724
 
3.0%
nyc 160151
 
2.8%
parks 154361
 
2.7%
location 120302
 
2.1%
of 118315
 
2.1%
a 110086
 
2.0%
will 102314
 
1.8%
Other values (210) 3808139
67.5%

Most occurring characters

ValueCountFrequency (%)
5613282
15.5%
e 3972441
 
11.0%
t 3014787
 
8.3%
i 2295515
 
6.4%
r 2278440
 
6.3%
n 2186126
 
6.1%
a 2135010
 
5.9%
o 1918190
 
5.3%
s 1785385
 
4.9%
c 944675
 
2.6%
Other values (47) 9971610
27.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 28350150
78.5%
Space Separator 5613282
 
15.5%
Uppercase Letter 1376088
 
3.8%
Other Punctuation 721125
 
2.0%
Control 34262
 
0.1%
Decimal Number 12164
 
< 0.1%
Open Punctuation 4195
 
< 0.1%
Close Punctuation 4195
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3972441
14.0%
t 3014787
10.6%
i 2295515
 
8.1%
r 2278440
 
8.0%
n 2186126
 
7.7%
a 2135010
 
7.5%
o 1918190
 
6.8%
s 1785385
 
6.3%
c 944675
 
3.3%
h 942582
 
3.3%
Other values (17) 6876999
24.3%
Uppercase Letter
ValueCountFrequency (%)
P 229354
16.7%
T 216378
15.7%
Y 179476
13.0%
N 164649
12.0%
C 160173
11.6%
R 107507
7.8%
M 67064
 
4.9%
D 58338
 
4.2%
U 34262
 
2.5%
F 34262
 
2.5%
Other values (8) 124625
9.1%
Other Punctuation
ValueCountFrequency (%)
. 384993
53.4%
, 165095
22.9%
/ 153854
 
21.3%
' 17183
 
2.4%
Decimal Number
ValueCountFrequency (%)
2 6030
49.6%
4 6030
49.6%
5 104
 
0.9%
Control
ValueCountFrequency (%)
™ 17131
50.0%
€ 17131
50.0%
Space Separator
ValueCountFrequency (%)
5613282
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4195
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4195
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 29726238
82.3%
Common 6389223
 
17.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3972441
13.4%
t 3014787
 
10.1%
i 2295515
 
7.7%
r 2278440
 
7.7%
n 2186126
 
7.4%
a 2135010
 
7.2%
o 1918190
 
6.5%
s 1785385
 
6.0%
c 944675
 
3.2%
h 942582
 
3.2%
Other values (35) 8253087
27.8%
Common
ValueCountFrequency (%)
5613282
87.9%
. 384993
 
6.0%
, 165095
 
2.6%
/ 153854
 
2.4%
' 17183
 
0.3%
™ 17131
 
0.3%
€ 17131
 
0.3%
2 6030
 
0.1%
4 6030
 
0.1%
( 4195
 
0.1%
Other values (2) 4299
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36064068
99.9%
None 51393
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5613282
15.6%
e 3972441
11.0%
t 3014787
 
8.4%
i 2295515
 
6.4%
r 2278440
 
6.3%
n 2186126
 
6.1%
a 2135010
 
5.9%
o 1918190
 
5.3%
s 1785385
 
5.0%
c 944675
 
2.6%
Other values (44) 9920217
27.5%
None
ValueCountFrequency (%)
™ 17131
33.3%
€ 17131
33.3%
â 17131
33.3%
Distinct93787
Distinct (%)72.2%
Missing14307
Missing (%)9.9%
Memory size1.1 MiB
Minimum2016-01-04 08:03:06
Maximum2023-11-22 14:55:05
2023-11-28T12:03:55.608743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:55.714012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct74
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2023-11-28T12:03:55.815117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length25
Median length21
Mean length10.862431
Min length8

Characters and Unicode

Total characters1566645
Distinct characters37
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11 MANHATTAN
2nd row09 MANHATTAN
3rd row11 MANHATTAN
4th row11 MANHATTAN
5th row01 BROOKLYN
ValueCountFrequency (%)
brooklyn 56851
19.1%
queens 34189
 
11.5%
manhattan 30375
 
10.2%
01 14646
 
4.9%
03 13647
 
4.6%
bronx 11786
 
4.0%
08 11503
 
3.9%
02 11213
 
3.8%
12 10720
 
3.6%
06 9885
 
3.3%
Other values (25) 93407
31.3%
2023-11-28T12:03:56.015902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 183116
 
11.7%
153996
 
9.8%
O 125488
 
8.0%
A 110665
 
7.1%
0 103095
 
6.6%
T 80290
 
5.1%
E 78148
 
5.0%
1 71160
 
4.5%
B 68637
 
4.4%
R 68637
 
4.4%
Other values (27) 523413
33.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1110982
70.9%
Decimal Number 286717
 
18.3%
Space Separator 153996
 
9.8%
Lowercase Letter 14950
 
1.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 183116
16.5%
O 125488
11.3%
A 110665
10.0%
T 80290
 
7.2%
E 78148
 
7.0%
B 68637
 
6.2%
R 68637
 
6.2%
L 66621
 
6.0%
K 56851
 
5.1%
Y 56851
 
5.1%
Other values (8) 215678
19.4%
Decimal Number
ValueCountFrequency (%)
0 103095
36.0%
1 71160
24.8%
2 21948
 
7.7%
3 17703
 
6.2%
5 16593
 
5.8%
8 14196
 
5.0%
4 13309
 
4.6%
7 11161
 
3.9%
6 10429
 
3.6%
9 7123
 
2.5%
Lowercase Letter
ValueCountFrequency (%)
e 2990
20.0%
i 2990
20.0%
n 1495
10.0%
s 1495
10.0%
p 1495
10.0%
c 1495
10.0%
f 1495
10.0%
d 1495
10.0%
Space Separator
ValueCountFrequency (%)
153996
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1125932
71.9%
Common 440713
 
28.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 183116
16.3%
O 125488
11.1%
A 110665
9.8%
T 80290
 
7.1%
E 78148
 
6.9%
B 68637
 
6.1%
R 68637
 
6.1%
L 66621
 
5.9%
K 56851
 
5.0%
Y 56851
 
5.0%
Other values (16) 230628
20.5%
Common
ValueCountFrequency (%)
153996
34.9%
0 103095
23.4%
1 71160
16.1%
2 21948
 
5.0%
3 17703
 
4.0%
5 16593
 
3.8%
8 14196
 
3.2%
4 13309
 
3.0%
7 11161
 
2.5%
6 10429
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1566645
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 183116
 
11.7%
153996
 
9.8%
O 125488
 
8.0%
A 110665
 
7.1%
0 103095
 
6.6%
T 80290
 
5.1%
E 78148
 
5.0%
1 71160
 
4.5%
B 68637
 
4.4%
R 68637
 
4.4%
Other values (27) 523413
33.4%

BBL
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct70703
Distinct (%)57.5%
Missing21174
Missing (%)14.7%
Infinite0
Infinite (%)0.0%
Mean2.9183194 × 109
Minimum0
Maximum5.20045 × 109
Zeros58
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-11-28T12:03:56.121256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.0071 × 109
Q12.0407775 × 109
median3.05083 × 109
Q34.02729 × 109
95-th percentile5.01715 × 109
Maximum5.20045 × 109
Range5.20045 × 109
Interquartile range (IQR)1.9865125 × 109

Descriptive statistics

Standard deviation1.2168899 × 109
Coefficient of variation (CV)0.41698312
Kurtosis-0.83784517
Mean2.9183194 × 109
Median Absolute Deviation (MAD)9.8728001 × 108
Skewness-0.27922614
Sum3.5910504 × 1014
Variance1.4808211 × 1018
MonotonicityNot monotonic
2023-11-28T12:03:56.227220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2046530041 965
 
0.7%
2046530048 162
 
0.1%
3019020001 120
 
0.1%
2046530090 105
 
0.1%
2024930001 88
 
0.1%
2046450006 87
 
0.1%
2046660049 86
 
0.1%
1009720001 82
 
0.1%
1011040031 82
 
0.1%
3019440004 80
 
0.1%
Other values (70693) 121195
84.0%
(Missing) 21174
 
14.7%
ValueCountFrequency (%)
0 58
< 0.1%
1000010010 1
 
< 0.1%
1000030001 2
 
< 0.1%
1000057501 1
 
< 0.1%
1000100023 1
 
< 0.1%
1000130027 1
 
< 0.1%
1000150022 1
 
< 0.1%
1000157501 1
 
< 0.1%
1000160001 2
 
< 0.1%
1000160100 1
 
< 0.1%
ValueCountFrequency (%)
5200449999 1
 
< 0.1%
5200399999 1
 
< 0.1%
5200379999 1
 
< 0.1%
5200169999 1
 
< 0.1%
5200059999 1
 
< 0.1%
5200029999 1
 
< 0.1%
5200009999 1
 
< 0.1%
5080500058 1
 
< 0.1%
5080500004 1
 
< 0.1%
5080490055 4
< 0.1%

Borough
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
BROOKLYN
56851 
QUEENS
34186 
MANHATTAN
30441 
BRONX
11723 
STATEN ISLAND
9770 

Length

Max length13
Median length11
Mean length7.857966
Min length5

Characters and Unicode

Total characters1133323
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMANHATTAN
2nd rowMANHATTAN
3rd rowMANHATTAN
4th rowMANHATTAN
5th rowBROOKLYN

Common Values

ValueCountFrequency (%)
BROOKLYN 56851
39.4%
QUEENS 34186
23.7%
MANHATTAN 30441
21.1%
BRONX 11723
 
8.1%
STATEN ISLAND 9770
 
6.8%
Unspecified 1255
 
0.9%

Length

2023-11-28T12:03:56.321900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T12:03:56.424052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
brooklyn 56851
36.9%
queens 34186
22.2%
manhattan 30441
19.8%
bronx 11723
 
7.6%
staten 9770
 
6.3%
island 9770
 
6.3%
unspecified 1255
 
0.8%

Most occurring characters

ValueCountFrequency (%)
N 183182
16.2%
O 125425
11.1%
A 110863
9.8%
T 80422
 
7.1%
E 78142
 
6.9%
B 68574
 
6.1%
R 68574
 
6.1%
L 66621
 
5.9%
K 56851
 
5.0%
Y 56851
 
5.0%
Other values (17) 237818
21.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1111003
98.0%
Lowercase Letter 12550
 
1.1%
Space Separator 9770
 
0.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 183182
16.5%
O 125425
11.3%
A 110863
10.0%
T 80422
 
7.2%
E 78142
 
7.0%
B 68574
 
6.2%
R 68574
 
6.2%
L 66621
 
6.0%
K 56851
 
5.1%
Y 56851
 
5.1%
Other values (8) 215498
19.4%
Lowercase Letter
ValueCountFrequency (%)
e 2510
20.0%
i 2510
20.0%
n 1255
10.0%
s 1255
10.0%
p 1255
10.0%
c 1255
10.0%
f 1255
10.0%
d 1255
10.0%
Space Separator
ValueCountFrequency (%)
9770
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1123553
99.1%
Common 9770
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 183182
16.3%
O 125425
11.2%
A 110863
9.9%
T 80422
 
7.2%
E 78142
 
7.0%
B 68574
 
6.1%
R 68574
 
6.1%
L 66621
 
5.9%
K 56851
 
5.1%
Y 56851
 
5.1%
Other values (16) 228048
20.3%
Common
ValueCountFrequency (%)
9770
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1133323
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 183182
16.2%
O 125425
11.1%
A 110863
9.8%
T 80422
 
7.1%
E 78142
 
6.9%
B 68574
 
6.1%
R 68574
 
6.1%
L 66621
 
5.9%
K 56851
 
5.0%
Y 56851
 
5.0%
Other values (17) 237818
21.0%

X Coordinate (State Plane)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct52133
Distinct (%)36.7%
Missing2022
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean1001126.4
Minimum913390
Maximum1067055
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-11-28T12:03:56.522634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum913390
5-th percentile953635
Q1989685
median998246.5
Q31014110
95-th percentile1043721.8
Maximum1067055
Range153665
Interquartile range (IQR)24425

Descriptive statistics

Standard deviation23607.599
Coefficient of variation (CV)0.023581036
Kurtosis1.3994679
Mean1001126.4
Median Absolute Deviation (MAD)10645.5
Skewness-0.23956837
Sum1.4236418 × 1011
Variance5.5731871 × 108
MonotonicityNot monotonic
2023-11-28T12:03:56.623465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1021287 788
 
0.5%
1018045 272
 
0.2%
1021399 178
 
0.1%
1021305 162
 
0.1%
1021515 106
 
0.1%
1020790 87
 
0.1%
1004038 85
 
0.1%
1022130 80
 
0.1%
1021980 76
 
0.1%
1018543 65
 
< 0.1%
Other values (52123) 140305
97.3%
(Missing) 2022
 
1.4%
ValueCountFrequency (%)
913390 1
< 0.1%
913803 1
< 0.1%
913861 1
< 0.1%
913862 1
< 0.1%
913875 1
< 0.1%
913879 1
< 0.1%
914030 1
< 0.1%
914047 1
< 0.1%
914231 1
< 0.1%
914241 1
< 0.1%
ValueCountFrequency (%)
1067055 1
< 0.1%
1067013 1
< 0.1%
1066926 1
< 0.1%
1066924 1
< 0.1%
1066922 1
< 0.1%
1066844 1
< 0.1%
1066838 1
< 0.1%
1066837 1
< 0.1%
1066836 2
< 0.1%
1066830 2
< 0.1%

Y Coordinate (State Plane)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct61903
Distinct (%)43.5%
Missing2016
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean196130.75
Minimum121156
Maximum271660
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-11-28T12:03:56.726274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum121156
5-th percentile150148
Q1173256.25
median195190.5
Q3216788
95-th percentile251389
Maximum271660
Range150504
Interquartile range (IQR)43531.75

Descriptive statistics

Standard deviation30110.027
Coefficient of variation (CV)0.15352018
Kurtosis-0.48655416
Mean196130.75
Median Absolute Deviation (MAD)21730.5
Skewness0.18505747
Sum2.7891754 × 1010
Variance9.0661376 × 108
MonotonicityNot monotonic
2023-11-28T12:03:56.822425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
261965 787
 
0.5%
246472 272
 
0.2%
262236 178
 
0.1%
262009 162
 
0.1%
261846 105
 
0.1%
260124 87
 
0.1%
241325 83
 
0.1%
261076 81
 
0.1%
262965 74
 
0.1%
258848 65
 
< 0.1%
Other values (61893) 140316
97.3%
(Missing) 2016
 
1.4%
ValueCountFrequency (%)
121156 2
< 0.1%
121168 1
 
< 0.1%
121189 1
 
< 0.1%
121245 1
 
< 0.1%
121292 1
 
< 0.1%
121295 3
< 0.1%
121315 1
 
< 0.1%
121405 1
 
< 0.1%
121480 1
 
< 0.1%
121488 1
 
< 0.1%
ValueCountFrequency (%)
271660 1
< 0.1%
271515 2
< 0.1%
271512 1
< 0.1%
271498 1
< 0.1%
271409 1
< 0.1%
271316 1
< 0.1%
271233 1
< 0.1%
271219 1
< 0.1%
271217 1
< 0.1%
271183 1
< 0.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
UNKNOWN
53117 
MOBILE
33208 
OTHER
25970 
PHONE
25076 
ONLINE
6855 

Length

Max length7
Median length6
Mean length6.0143594
Min length5

Characters and Unicode

Total characters867427
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMOBILE
2nd rowUNKNOWN
3rd rowUNKNOWN
4th rowUNKNOWN
5th rowMOBILE

Common Values

ValueCountFrequency (%)
UNKNOWN 53117
36.8%
MOBILE 33208
23.0%
OTHER 25970
18.0%
PHONE 25076
17.4%
ONLINE 6855
 
4.8%

Length

2023-11-28T12:03:56.922766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T12:03:57.022922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
unknown 53117
36.8%
mobile 33208
23.0%
other 25970
18.0%
phone 25076
17.4%
online 6855
 
4.8%

Most occurring characters

ValueCountFrequency (%)
N 198137
22.8%
O 144226
16.6%
E 91109
10.5%
U 53117
 
6.1%
K 53117
 
6.1%
W 53117
 
6.1%
H 51046
 
5.9%
I 40063
 
4.6%
L 40063
 
4.6%
M 33208
 
3.8%
Other values (4) 110224
12.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 867427
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 198137
22.8%
O 144226
16.6%
E 91109
10.5%
U 53117
 
6.1%
K 53117
 
6.1%
W 53117
 
6.1%
H 51046
 
5.9%
I 40063
 
4.6%
L 40063
 
4.6%
M 33208
 
3.8%
Other values (4) 110224
12.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 867427
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 198137
22.8%
O 144226
16.6%
E 91109
10.5%
U 53117
 
6.1%
K 53117
 
6.1%
W 53117
 
6.1%
H 51046
 
5.9%
I 40063
 
4.6%
L 40063
 
4.6%
M 33208
 
3.8%
Other values (4) 110224
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 867427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 198137
22.8%
O 144226
16.6%
E 91109
10.5%
U 53117
 
6.1%
K 53117
 
6.1%
W 53117
 
6.1%
H 51046
 
5.9%
I 40063
 
4.6%
L 40063
 
4.6%
M 33208
 
3.8%
Other values (4) 110224
12.7%
Distinct309
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2023-11-28T12:03:57.102742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length49
Median length11
Mean length11.039244
Min length9

Characters and Unicode

Total characters1592146
Distinct characters65
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique183 ?
Unique (%)0.1%

Sample

1st rowUnspecified
2nd rowUnspecified
3rd rowUnspecified
4th rowUnspecified
5th rowUnspecified
ValueCountFrequency (%)
unspecified 143439
98.5%
park 524
 
0.4%
playground 180
 
0.1%
bronx 87
 
0.1%
corona 71
 
< 0.1%
flushing 67
 
< 0.1%
meadows 67
 
< 0.1%
ogden 33
 
< 0.1%
plimpton 33
 
< 0.1%
square 22
 
< 0.1%
Other values (439) 1074
 
0.7%
2023-11-28T12:03:57.303513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 287712
18.1%
i 287335
18.0%
n 144394
9.1%
d 143917
9.0%
s 143890
9.0%
c 143568
9.0%
p 143512
9.0%
f 143480
9.0%
U 143449
9.0%
r 1429
 
0.1%
Other values (55) 9460
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1445131
90.8%
Uppercase Letter 145589
 
9.1%
Space Separator 1371
 
0.1%
Other Punctuation 32
 
< 0.1%
Decimal Number 15
 
< 0.1%
Dash Punctuation 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 287712
19.9%
i 287335
19.9%
n 144394
10.0%
d 143917
10.0%
s 143890
10.0%
c 143568
9.9%
p 143512
9.9%
f 143480
9.9%
r 1429
 
0.1%
a 1367
 
0.1%
Other values (16) 4527
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
U 143449
98.5%
P 799
 
0.5%
B 215
 
0.1%
C 200
 
0.1%
F 130
 
0.1%
M 128
 
0.1%
A 97
 
0.1%
S 81
 
0.1%
H 57
 
< 0.1%
G 56
 
< 0.1%
Other values (14) 377
 
0.3%
Decimal Number
ValueCountFrequency (%)
4 6
40.0%
8 3
20.0%
3 1
 
6.7%
5 1
 
6.7%
7 1
 
6.7%
9 1
 
6.7%
1 1
 
6.7%
6 1
 
6.7%
Other Punctuation
ValueCountFrequency (%)
. 26
81.2%
& 2
 
6.2%
/ 2
 
6.2%
' 1
 
3.1%
, 1
 
3.1%
Space Separator
ValueCountFrequency (%)
1371
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1590720
99.9%
Common 1426
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 287712
18.1%
i 287335
18.1%
n 144394
9.1%
d 143917
9.0%
s 143890
9.0%
c 143568
9.0%
p 143512
9.0%
f 143480
9.0%
U 143449
9.0%
r 1429
 
0.1%
Other values (40) 8034
 
0.5%
Common
ValueCountFrequency (%)
1371
96.1%
. 26
 
1.8%
- 8
 
0.6%
4 6
 
0.4%
8 3
 
0.2%
& 2
 
0.1%
/ 2
 
0.1%
3 1
 
0.1%
5 1
 
0.1%
7 1
 
0.1%
Other values (5) 5
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1592146
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 287712
18.1%
i 287335
18.0%
n 144394
9.1%
d 143917
9.0%
s 143890
9.0%
c 143568
9.0%
p 143512
9.0%
f 143480
9.0%
U 143449
9.0%
r 1429
 
0.1%
Other values (55) 9460
 
0.6%

Park Borough
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
BROOKLYN
56851 
QUEENS
34186 
MANHATTAN
30441 
BRONX
11723 
STATEN ISLAND
9770 

Length

Max length13
Median length11
Mean length7.857966
Min length5

Characters and Unicode

Total characters1133323
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMANHATTAN
2nd rowMANHATTAN
3rd rowMANHATTAN
4th rowMANHATTAN
5th rowBROOKLYN

Common Values

ValueCountFrequency (%)
BROOKLYN 56851
39.4%
QUEENS 34186
23.7%
MANHATTAN 30441
21.1%
BRONX 11723
 
8.1%
STATEN ISLAND 9770
 
6.8%
Unspecified 1255
 
0.9%

Length

2023-11-28T12:03:57.400770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T12:03:57.504026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
brooklyn 56851
36.9%
queens 34186
22.2%
manhattan 30441
19.8%
bronx 11723
 
7.6%
staten 9770
 
6.3%
island 9770
 
6.3%
unspecified 1255
 
0.8%

Most occurring characters

ValueCountFrequency (%)
N 183182
16.2%
O 125425
11.1%
A 110863
9.8%
T 80422
 
7.1%
E 78142
 
6.9%
B 68574
 
6.1%
R 68574
 
6.1%
L 66621
 
5.9%
K 56851
 
5.0%
Y 56851
 
5.0%
Other values (17) 237818
21.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1111003
98.0%
Lowercase Letter 12550
 
1.1%
Space Separator 9770
 
0.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 183182
16.5%
O 125425
11.3%
A 110863
10.0%
T 80422
 
7.2%
E 78142
 
7.0%
B 68574
 
6.2%
R 68574
 
6.2%
L 66621
 
6.0%
K 56851
 
5.1%
Y 56851
 
5.1%
Other values (8) 215498
19.4%
Lowercase Letter
ValueCountFrequency (%)
e 2510
20.0%
i 2510
20.0%
n 1255
10.0%
s 1255
10.0%
p 1255
10.0%
c 1255
10.0%
f 1255
10.0%
d 1255
10.0%
Space Separator
ValueCountFrequency (%)
9770
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1123553
99.1%
Common 9770
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 183182
16.3%
O 125425
11.2%
A 110863
9.9%
T 80422
 
7.2%
E 78142
 
7.0%
B 68574
 
6.1%
R 68574
 
6.1%
L 66621
 
5.9%
K 56851
 
5.1%
Y 56851
 
5.1%
Other values (16) 228048
20.3%
Common
ValueCountFrequency (%)
9770
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1133323
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 183182
16.2%
O 125425
11.1%
A 110863
9.8%
T 80422
 
7.1%
E 78142
 
6.9%
B 68574
 
6.1%
R 68574
 
6.1%
L 66621
 
5.9%
K 56851
 
5.0%
Y 56851
 
5.0%
Other values (17) 237818
21.0%

Vehicle Type
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing144226
Missing (%)100.0%
Memory size1.1 MiB

Taxi Company Borough
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing144226
Missing (%)100.0%
Memory size1.1 MiB

Taxi Pick Up Location
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing144226
Missing (%)100.0%
Memory size1.1 MiB

Bridge Highway Name
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing144226
Missing (%)100.0%
Memory size1.1 MiB

Bridge Highway Direction
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing144226
Missing (%)100.0%
Memory size1.1 MiB

Road Ramp
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing144226
Missing (%)100.0%
Memory size1.1 MiB

Bridge Highway Segment
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing144226
Missing (%)100.0%
Memory size1.1 MiB

Latitude
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct94142
Distinct (%)66.2%
Missing2022
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean40.704961
Minimum40.498966
Maximum40.912275
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-11-28T12:03:57.606239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum40.498966
5-th percentile40.578782
Q140.64221
median40.702367
Q340.76168
95-th percentile40.856605
Maximum40.912275
Range0.41330961
Interquartile range (IQR)0.11946984

Descriptive statistics

Standard deviation0.082641132
Coefficient of variation (CV)0.0020302472
Kurtosis-0.48580506
Mean40.704961
Median Absolute Deviation (MAD)0.059640299
Skewness0.18480354
Sum5788408.3
Variance0.0068295567
MonotonicityNot monotonic
2023-11-28T12:03:57.705286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.88562974 787
 
0.5%
40.8431192 272
 
0.2%
40.88637308 178
 
0.1%
40.88575043 162
 
0.1%
40.88530216 105
 
0.1%
40.88057885 87
 
0.1%
40.82903468 82
 
0.1%
40.88318614 79
 
0.1%
40.88837149 74
 
0.1%
40.87708565 65
 
< 0.1%
Other values (94132) 140313
97.3%
(Missing) 2022
 
1.4%
ValueCountFrequency (%)
40.49896579 2
< 0.1%
40.49900071 1
 
< 0.1%
40.49905831 1
 
< 0.1%
40.49921196 1
 
< 0.1%
40.49934087 1
 
< 0.1%
40.49934905 3
< 0.1%
40.49940396 1
 
< 0.1%
40.49965278 1
 
< 0.1%
40.49985448 1
 
< 0.1%
40.49988251 1
 
< 0.1%
ValueCountFrequency (%)
40.9122754 1
< 0.1%
40.9118747 2
< 0.1%
40.91186643 1
< 0.1%
40.91182781 1
< 0.1%
40.91158732 1
< 0.1%
40.91133076 1
< 0.1%
40.91110034 1
< 0.1%
40.911063 1
< 0.1%
40.91105749 1
< 0.1%
40.91096477 1
< 0.1%

Longitude
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct94141
Distinct (%)66.2%
Missing2022
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean-73.939084
Minimum-74.254818
Maximum-73.70118
Zeros0
Zeros (%)0.0%
Negative142204
Negative (%)98.6%
Memory size1.1 MiB
2023-11-28T12:03:57.904438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-74.254818
5-th percentile-74.110275
Q1-73.980402
median-73.949536
Q3-73.892224
95-th percentile-73.785378
Maximum-73.70118
Range0.55363731
Interquartile range (IQR)0.088177868

Descriptive statistics

Standard deviation0.085127478
Coefficient of variation (CV)-0.0011513191
Kurtosis1.3894837
Mean-73.939084
Median Absolute Deviation (MAD)0.038408407
Skewness-0.23520793
Sum-10514434
Variance0.0072466876
MonotonicityNot monotonic
2023-11-28T12:03:58.000370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.86605482 787
 
0.5%
-73.8778577 272
 
0.2%
-73.86564827 178
 
0.1%
-73.86598948 162
 
0.1%
-73.86523092 105
 
0.1%
-73.86786228 87
 
0.1%
-73.92849713 82
 
0.1%
-73.86301113 79
 
0.1%
-73.86354294 74
 
0.1%
-73.87599451 65
 
< 0.1%
Other values (94131) 140313
97.3%
(Missing) 2022
 
1.4%
ValueCountFrequency (%)
-74.25481779 1
< 0.1%
-74.25333612 1
< 0.1%
-74.2531185 1
< 0.1%
-74.25311504 1
< 0.1%
-74.25306946 1
< 0.1%
-74.25305539 1
< 0.1%
-74.25250207 1
< 0.1%
-74.25244803 1
< 0.1%
-74.25178071 1
< 0.1%
-74.25174414 1
< 0.1%
ValueCountFrequency (%)
-73.70118048 1
< 0.1%
-73.70134026 1
< 0.1%
-73.7016175 1
< 0.1%
-73.70162521 1
< 0.1%
-73.70167632 1
< 0.1%
-73.70190128 1
< 0.1%
-73.70192299 1
< 0.1%
-73.70192648 1
< 0.1%
-73.70192983 1
< 0.1%
-73.70192989 1
< 0.1%

Location
Text

MISSING 

Distinct94142
Distinct (%)66.2%
Missing2022
Missing (%)1.4%
Memory size1.1 MiB
2023-11-28T12:03:58.195019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length40
Median length39
Mean length39.045125
Min length25

Characters and Unicode

Total characters5552373
Distinct characters16
Distinct categories6 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70214 ?
Unique (%)49.4%

Sample

1st row(40.784677372848186, -73.94369307178569)
2nd row(40.826286700852854, -73.94804079135497)
3rd row(40.79577555535228, -73.93556471483194)
4th row(40.79577555535228, -73.93556471483194)
5th row(40.707904412853836, -73.95060378045339)
ValueCountFrequency (%)
40.88562974190851 787
 
0.3%
73.86605482246202 787
 
0.3%
40.843119201779096 272
 
0.1%
73.8778577026157 272
 
0.1%
40.88637308028402 178
 
0.1%
73.86564826928695 178
 
0.1%
40.8857504324131 162
 
0.1%
73.86598948170207 162
 
0.1%
40.885302164741034 105
 
< 0.1%
73.86523091854339 105
 
< 0.1%
Other values (188273) 281400
98.9%
2023-11-28T12:03:58.486808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 578570
10.4%
4 543970
9.8%
0 492080
8.9%
3 489012
8.8%
9 452782
8.2%
6 440561
7.9%
8 421607
7.6%
5 402434
7.2%
1 369698
 
6.7%
2 366231
 
6.6%
Other values (6) 995428
17.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4556945
82.1%
Other Punctuation 426612
 
7.7%
Open Punctuation 142204
 
2.6%
Space Separator 142204
 
2.6%
Dash Punctuation 142204
 
2.6%
Close Punctuation 142204
 
2.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 578570
12.7%
4 543970
11.9%
0 492080
10.8%
3 489012
10.7%
9 452782
9.9%
6 440561
9.7%
8 421607
9.3%
5 402434
8.8%
1 369698
8.1%
2 366231
8.0%
Other Punctuation
ValueCountFrequency (%)
. 284408
66.7%
, 142204
33.3%
Open Punctuation
ValueCountFrequency (%)
( 142204
100.0%
Space Separator
ValueCountFrequency (%)
142204
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 142204
100.0%
Close Punctuation
ValueCountFrequency (%)
) 142204
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5552373
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
7 578570
10.4%
4 543970
9.8%
0 492080
8.9%
3 489012
8.8%
9 452782
8.2%
6 440561
7.9%
8 421607
7.6%
5 402434
7.2%
1 369698
 
6.7%
2 366231
 
6.6%
Other values (6) 995428
17.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5552373
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 578570
10.4%
4 543970
9.8%
0 492080
8.9%
3 489012
8.8%
9 452782
8.2%
6 440561
7.9%
8 421607
7.6%
5 402434
7.2%
1 369698
 
6.7%
2 366231
 
6.6%
Other values (6) 995428
17.9%

zip_codes
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct200
Distinct (%)0.1%
Missing2486
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean14675.147
Minimum10090
Maximum26001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-11-28T12:03:58.604756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10090
5-th percentile10693
Q112081
median13826
Q317212
95-th percentile24011
Maximum26001
Range15911
Interquartile range (IQR)5131

Descriptive statistics

Standard deviation3296.5541
Coefficient of variation (CV)0.22463518
Kurtosis1.2978498
Mean14675.147
Median Absolute Deviation (MAD)2102
Skewness1.1390536
Sum2.0800553 × 109
Variance10867269
MonotonicityNot monotonic
2023-11-28T12:03:58.706393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13512 5205
 
3.6%
13826 4400
 
3.1%
17617 3870
 
2.7%
15310 3555
 
2.5%
11605 2653
 
1.8%
13829 2550
 
1.8%
13513 2430
 
1.7%
17613 2249
 
1.6%
12083 1976
 
1.4%
10699 1919
 
1.3%
Other values (190) 110933
76.9%
(Missing) 2486
 
1.7%
ValueCountFrequency (%)
10090 655
0.5%
10091 21
 
< 0.1%
10092 782
0.5%
10094 5
 
< 0.1%
10098 47
 
< 0.1%
10099 1276
0.9%
10350 1
 
< 0.1%
10351 1
 
< 0.1%
10352 3
 
< 0.1%
10354 2
 
< 0.1%
ValueCountFrequency (%)
26001 1
 
< 0.1%
24672 7
 
< 0.1%
24671 233
 
0.2%
24670 410
0.3%
24669 357
0.2%
24668 302
0.2%
24340 722
0.5%
24339 5
 
< 0.1%
24338 214
 
0.1%
24337 244
 
0.2%

Community Districts
Real number (ℝ)

MISSING 

Distinct68
Distinct (%)< 0.1%
Missing2034
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean34.477629
Minimum1
Maximum71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-11-28T12:03:58.814968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q116
median32
Q354
95-th percentile69
Maximum71
Range70
Interquartile range (IQR)38

Descriptive statistics

Standard deviation21.028337
Coefficient of variation (CV)0.60991251
Kurtosis-1.1976096
Mean34.477629
Median Absolute Deviation (MAD)18
Skewness0.20805725
Sum4902443
Variance442.19096
MonotonicityNot monotonic
2023-11-28T12:03:58.911580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 9199
 
6.4%
23 5577
 
3.9%
14 5491
 
3.8%
54 5025
 
3.5%
15 4450
 
3.1%
36 4347
 
3.0%
12 4083
 
2.8%
2 3939
 
2.7%
68 3879
 
2.7%
69 3732
 
2.6%
Other values (58) 92470
64.1%
ValueCountFrequency (%)
1 2551
1.8%
2 3939
2.7%
4 2828
2.0%
5 2780
1.9%
6 490
 
0.3%
7 1479
 
1.0%
8 544
 
0.4%
9 2162
1.5%
10 2164
1.5%
11 1361
 
0.9%
ValueCountFrequency (%)
71 2160
1.5%
70 3007
2.1%
69 3732
2.6%
68 3879
2.7%
67 26
 
< 0.1%
66 1723
1.2%
65 2170
1.5%
64 2
 
< 0.1%
63 2353
1.6%
62 2565
1.8%

Borough Boundaries
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing2034
Missing (%)1.4%
Memory size1.1 MiB
2.0
56648 
3.0
33956 
4.0
30327 
5.0
11583 
1.0
9678 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters426576
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row4.0
4th row4.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 56648
39.3%
3.0 33956
23.5%
4.0 30327
21.0%
5.0 11583
 
8.0%
1.0 9678
 
6.7%
(Missing) 2034
 
1.4%

Length

2023-11-28T12:03:59.004640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T12:03:59.097186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 56648
39.8%
3.0 33956
23.9%
4.0 30327
21.3%
5.0 11583
 
8.1%
1.0 9678
 
6.8%

Most occurring characters

ValueCountFrequency (%)
. 142192
33.3%
0 142192
33.3%
2 56648
 
13.3%
3 33956
 
8.0%
4 30327
 
7.1%
5 11583
 
2.7%
1 9678
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 284384
66.7%
Other Punctuation 142192
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142192
50.0%
2 56648
 
19.9%
3 33956
 
11.9%
4 30327
 
10.7%
5 11583
 
4.1%
1 9678
 
3.4%
Other Punctuation
ValueCountFrequency (%)
. 142192
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 426576
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 142192
33.3%
0 142192
33.3%
2 56648
 
13.3%
3 33956
 
8.0%
4 30327
 
7.1%
5 11583
 
2.7%
1 9678
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 426576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 142192
33.3%
0 142192
33.3%
2 56648
 
13.3%
3 33956
 
8.0%
4 30327
 
7.1%
5 11583
 
2.7%
1 9678
 
2.3%

City Council Districts
Real number (ℝ)

MISSING 

Distinct51
Distinct (%)< 0.1%
Missing2034
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean25.669813
Minimum1
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-11-28T12:03:59.189388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113
median26
Q338
95-th percentile50
Maximum51
Range50
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.956067
Coefficient of variation (CV)0.58263248
Kurtosis-1.2132291
Mean25.669813
Median Absolute Deviation (MAD)12
Skewness0.14350992
Sum3650042
Variance223.68393
MonotonicityNot monotonic
2023-11-28T12:03:59.290738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 9844
 
6.8%
27 7205
 
5.0%
10 5757
 
4.0%
48 5114
 
3.5%
51 4358
 
3.0%
30 4342
 
3.0%
41 4265
 
3.0%
38 4145
 
2.9%
34 4095
 
2.8%
9 3930
 
2.7%
Other values (41) 89137
61.8%
ValueCountFrequency (%)
1 2870
2.0%
2 3000
2.1%
3 1496
 
1.0%
4 2518
1.7%
5 2627
1.8%
6 1485
 
1.0%
7 2072
 
1.4%
8 2741
1.9%
9 3930
2.7%
10 5757
4.0%
ValueCountFrequency (%)
51 4358
3.0%
50 3898
2.7%
49 3746
2.6%
48 5114
3.5%
47 1082
 
0.8%
46 1630
 
1.1%
45 2131
1.5%
44 2640
1.8%
43 1077
 
0.7%
42 648
 
0.4%

Police Precincts
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct77
Distinct (%)0.1%
Missing2034
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean43.390472
Minimum1
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-11-28T12:03:59.390750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q130
median44
Q362
95-th percentile75
Maximum77
Range76
Interquartile range (IQR)32

Descriptive statistics

Standard deviation21.555217
Coefficient of variation (CV)0.49677306
Kurtosis-0.97813679
Mean43.390472
Median Absolute Deviation (MAD)17
Skewness-0.28246768
Sum6169778
Variance464.6274
MonotonicityNot monotonic
2023-11-28T12:03:59.492262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36 9200
 
6.4%
11 5577
 
3.9%
62 5032
 
3.5%
50 4237
 
2.9%
39 3939
 
2.7%
76 3563
 
2.5%
30 3310
 
2.3%
69 3299
 
2.3%
72 2967
 
2.1%
43 2915
 
2.0%
Other values (67) 98153
68.1%
ValueCountFrequency (%)
1 1023
0.7%
2 761
 
0.5%
3 1787
1.2%
4 645
 
0.4%
5 2314
1.6%
6 1455
1.0%
7 1819
1.3%
8 549
 
0.4%
9 1273
0.9%
10 2509
1.7%
ValueCountFrequency (%)
77 2484
1.7%
76 3563
2.5%
75 1797
1.2%
74 1834
1.3%
73 2169
1.5%
72 2967
2.1%
71 993
 
0.7%
70 1583
1.1%
69 3299
2.3%
68 1751
1.2%

Interactions

2023-11-28T12:03:45.302316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:34.933980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:36.074601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:37.100873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:38.077327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:39.151063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:40.187346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:41.240127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:42.220619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:43.245840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:44.245223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:45.392397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:35.032699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:36.164644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:37.190506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:38.173102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:39.245875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:40.271401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:41.326734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:42.313503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:43.334036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:44.330956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:45.481708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:35.122080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:36.254324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:37.273658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:38.267884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:39.336644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:40.356738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:41.414735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:42.404580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:43.423064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:44.416636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:45.569051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:35.208235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:36.344349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:37.359679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:38.361769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:39.427489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:40.441907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:41.501223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:42.492729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:43.509101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:44.499699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:45.668768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:35.305573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:36.444480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:37.453961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:38.459553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:39.525235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:40.629774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:41.595485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:42.591754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:43.606059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:44.593495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:45.763813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:35.401018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:36.542422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:37.545458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:38.558520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:39.620678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:40.721270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:41.687419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:42.687447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:43.700072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:44.684528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:45.850807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:35.489567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:36.631909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:37.630159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:38.648996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:39.710230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:40.800764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:41.772013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:42.775407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:43.785181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:44.767775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:45.940159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:35.576501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:36.724218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:37.716596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:38.748374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:39.802549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:40.885546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:41.856664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:42.865363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:43.874077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:44.852829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:46.037633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:35.671772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:36.822837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:37.810664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:38.854962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:39.902819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:40.977488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:41.950958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:42.962267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:43.971485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:45.041175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:46.132505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:35.764787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:36.916695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:37.899575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:38.953955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:39.998689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:41.065971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:42.042098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:43.058051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:44.063177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:45.129214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:46.219276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:35.976850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:37.003288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:37.983939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:39.047505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:40.088734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:41.148707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:42.126548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:43.146760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:44.149784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T12:03:45.211818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-11-28T12:03:59.595878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Address TypeAgencyBBLBoroughBorough BoundariesCityCity Council DistrictsCommunity DistrictsLatitudeLongitudeOpen Data Channel TypePark BoroughPolice PrecinctsResolution DescriptionStatusUnique KeyX Coordinate (State Plane)Y Coordinate (State Plane)agency_nameincident_ziplocation_typezip_codes
Address Type1.0000.0000.0180.0370.0400.0550.057-0.0080.0410.0040.3290.037-0.0310.4340.090-0.2450.0040.0410.000-0.0130.3190.011
Agency0.0001.0000.0020.0000.0000.036-0.0040.0040.0020.0030.0020.0000.0040.0000.011-0.0030.0030.0020.5000.0030.0000.001
BBL0.0180.0021.0000.8941.0000.726-0.225-0.008-0.5210.3070.1050.8940.8660.1020.057-0.0290.307-0.5200.0000.6810.0720.337
Borough0.0370.0000.8941.0000.9990.897-0.1310.092-0.0060.0680.1121.0000.5100.2670.0700.0210.068-0.0060.0000.1030.123-0.040
Borough Boundaries0.0400.0001.0000.9991.0000.9990.0890.2270.8690.3810.0980.999-0.5420.1500.0700.0310.3800.8690.000-0.3320.087-0.340
City0.0550.0360.7260.8970.9991.0000.0180.018-0.010-0.1930.1480.8970.1240.0620.0840.024-0.193-0.0100.036-0.1630.097-0.232
City Council Districts0.057-0.004-0.225-0.1310.0890.0181.0000.2620.0630.0170.0930.361-0.1340.0870.058-0.0360.0170.0630.0010.0080.0660.251
Community Districts-0.0080.004-0.0080.0920.2270.0180.2621.0000.1880.3760.0970.3750.0840.0940.071-0.0050.3760.1880.0000.1740.0430.136
Latitude0.0410.002-0.521-0.0060.869-0.0100.0630.1881.0000.3960.1000.594-0.3380.0970.0620.0390.3951.0000.000-0.3410.088-0.286
Longitude0.0040.0030.3070.0680.381-0.1930.0170.3760.3961.0000.1160.6150.2630.0930.050-0.0241.0000.3960.0000.5730.0540.311
Open Data Channel Type0.3290.0020.1050.1120.0980.1480.0930.0970.1000.1161.0000.1120.0360.3750.2780.225-0.034-0.0490.002-0.0170.143-0.062
Park Borough0.0370.0000.8941.0000.9990.8970.3610.3750.5940.6150.1121.0000.5100.2670.0700.0210.068-0.0060.0000.1030.123-0.040
Police Precincts-0.0310.0040.8660.510-0.5420.124-0.1340.084-0.3380.2630.0360.5101.0000.1180.082-0.0090.264-0.3380.0000.6080.0850.401
Resolution Description0.4340.0000.1020.2670.1500.0620.0870.0940.0970.0930.3750.2670.1181.0000.649-0.432-0.0100.0050.000-0.0270.3310.002
Status0.0900.0110.0570.0700.0700.0840.0580.0710.0620.0500.2780.0700.0820.6491.0000.3800.0150.0340.0110.0020.057-0.014
Unique Key-0.245-0.003-0.0290.0210.0310.024-0.036-0.0050.039-0.0240.2250.021-0.009-0.4320.3801.000-0.0240.0390.000-0.0370.228-0.042
X Coordinate (State Plane)0.0040.0030.3070.0680.380-0.1930.0170.3760.3951.000-0.0340.0680.264-0.0100.015-0.0241.0000.3960.0000.5740.0540.312
Y Coordinate (State Plane)0.0410.002-0.520-0.0060.869-0.0100.0630.1881.0000.396-0.049-0.006-0.3380.0050.0340.0390.3961.0000.000-0.3410.088-0.286
agency_name0.0000.5000.0000.0000.0000.0360.0010.0000.0000.0000.0020.0000.0000.0000.0110.0000.0000.0001.0000.0030.0000.001
incident_zip-0.0130.0030.6810.103-0.332-0.1630.0080.174-0.3410.573-0.0170.1030.608-0.0270.002-0.0370.574-0.3410.0031.0000.0520.653
location_type0.3190.0000.0720.1230.0870.0970.0660.0430.0880.0540.1430.1230.0850.3310.0570.2280.0540.0880.0000.0521.0000.029
zip_codes0.0110.0010.337-0.040-0.340-0.2320.2510.136-0.2860.311-0.062-0.0400.4010.002-0.014-0.0420.312-0.2860.0010.6530.0291.000

Missing values

2023-11-28T12:03:46.519604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-28T12:03:47.359759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-11-28T12:03:48.604158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unique Keycreated_dateclosed_dateAgencyagency_nameComplaint TypeDescriptorlocation_typeincident_zipIncident AddressStreet NameCross Street 1Cross Street 2Intersection Street 1Intersection Street 2Address TypeCityLandmarkFacility TypeStatusDue DateResolution DescriptionResolution Action Updated DateCommunity BoardBBLBoroughX Coordinate (State Plane)Y Coordinate (State Plane)Open Data Channel TypePark Facility NamePark BoroughVehicle TypeTaxi Company BoroughTaxi Pick Up LocationBridge Highway NameBridge Highway DirectionRoad RampBridge Highway SegmentLatitudeLongitudeLocationzip_codesCommunity DistrictsBorough BoundariesCity Council DistrictsPolice Precincts
05954491811/25/2023 08:28:12 PMNaNDPRDepartment of Parks and RecreationNew Tree RequestFor One AddressStreet10029.01907 1 AVENUE1 AVENUEEAST 97 STREETEAST 99 STREETEAST 97 STREETEAST 99 STREETADDRESSNEW YORK1 AVENUENaNIn ProgressNaNNaNNaN11 MANHATTANNaNMANHATTAN999843.0225161.0MOBILEUnspecifiedMANHATTANNaNNaNNaNNaNNaNNaNNaN40.784677-73.943693(40.784677372848186, -73.94369307178569)12426.07.04.035.014.0
15954387211/25/2023 07:35:34 PMNaNDPRDepartment of Parks and RecreationNew Tree RequestFor One AddressStreet10031.0526 WEST 146 STREETWEST 146 STREETAMSTERDAM AVENUEBROADWAYAMSTERDAM AVENUEBROADWAYADDRESSNEW YORKWEST 146 STREETNaNIn ProgressNaNNaNNaN09 MANHATTAN1.020778e+09MANHATTAN998630.0240320.0UNKNOWNUnspecifiedMANHATTANNaNNaNNaNNaNNaNNaNNaN40.826287-73.948041(40.826286700852854, -73.94804079135497)12428.037.04.023.019.0
25954916111/25/2023 06:20:15 PMNaNDPRDepartment of Parks and RecreationNew Tree RequestFor One AddressStreet10029.02252 1 AVENUE1 AVENUEEAST 115 STREETEAST 116 STREETEAST 115 STREETEAST 116 STREETADDRESSNEW YORK1 AVENUENaNIn ProgressNaNNaNNaN11 MANHATTAN1.017090e+09MANHATTAN1002091.0229206.0UNKNOWNUnspecifiedMANHATTANNaNNaNNaNNaNNaNNaNNaN40.795776-73.935565(40.79577555535228, -73.93556471483194)12426.07.04.035.016.0
35954916311/25/2023 06:16:56 PMNaNDPRDepartment of Parks and RecreationNew Tree RequestFor One AddressStreet10029.02252 1 AVENUE1 AVENUEEAST 115 STREETEAST 116 STREETEAST 115 STREETEAST 116 STREETADDRESSNEW YORK1 AVENUENaNIn ProgressNaNNaNNaN11 MANHATTAN1.017090e+09MANHATTAN1002091.0229206.0UNKNOWNUnspecifiedMANHATTANNaNNaNNaNNaNNaNNaNNaN40.795776-73.935565(40.79577555535228, -73.93556471483194)12426.07.04.035.016.0
45955021411/25/2023 04:53:12 PMNaNDPRDepartment of Parks and RecreationNew Tree RequestFor One AddressStreet11211.0250 UNION AVENUEUNION AVENUEMESEROLE STREETSCHOLES STREETMESEROLE STREETSCHOLES STREETADDRESSBROOKLYNUNION AVENUENaNIn ProgressNaNNaNNaN01 BROOKLYN3.030400e+09BROOKLYN997945.0197189.0MOBILEUnspecifiedBROOKLYNNaNNaNNaNNaNNaNNaNNaN40.707904-73.950604(40.707904412853836, -73.95060378045339)17213.036.02.030.056.0
55954598611/25/2023 04:10:24 PMNaNDPRDepartment of Parks and RecreationNew Tree RequestFor One AddressStreet10128.0121 EAST 90 STREETEAST 90 STREETPARK AVENUELEXINGTON AVENUEPARK AVENUELEXINGTON AVENUEADDRESSNEW YORKEAST 90 STREETNaNIn ProgressNaNNaNNaN08 MANHATTAN1.015190e+09MANHATTAN996876.0224314.0UNKNOWNUnspecifiedMANHATTANNaNNaNNaNNaNNaNNaNNaN40.782357-73.954409(40.7823573201046, -73.9544086136535)10099.023.04.051.011.0
65954387311/25/2023 02:00:31 PMNaNDPRDepartment of Parks and RecreationNew Tree RequestFor One AddressStreet11217.094 6 AVENUE6 AVENUEST MARKS AVENUEPROSPECT PLACEST MARKS AVENUEPROSPECT PLACEADDRESSBROOKLYN6 AVENUENaNIn ProgressNaNNaNNaN06 BROOKLYN3.009350e+09BROOKLYN991117.0186827.0MOBILEUnspecifiedBROOKLYNNaNNaNNaNNaNNaNNaNNaN40.679471-73.975242(40.6794710564277, -73.97524211737513)17619.014.02.027.050.0
75954810311/25/2023 01:59:52 PMNaNDPRDepartment of Parks and RecreationNew Tree RequestFor One AddressStreet11217.092 6 AVENUE6 AVENUEST MARKS AVENUEPROSPECT PLACEST MARKS AVENUEPROSPECT PLACEADDRESSBROOKLYN6 AVENUENaNIn ProgressNaNNaNNaN06 BROOKLYN3.009350e+09BROOKLYN991127.0186847.0MOBILEUnspecifiedBROOKLYNNaNNaNNaNNaNNaNNaNNaN40.679526-73.975206(40.679525944129665, -73.97520604354887)17619.014.02.027.050.0
85954387411/25/2023 01:29:20 PMNaNDPRDepartment of Parks and RecreationNew Tree RequestFor One AddressStreet11206.0GRAHAM AVENUEGRAHAM AVENUEGRAHAM AVENUEMONTROSE AVENUEGRAHAM AVENUEMONTROSE AVENUEINTERSECTIONNaNNaNNaNIn ProgressNaNNaNNaN01 BROOKLYNNaNBROOKLYN999997.0196983.0PHONEUnspecifiedBROOKLYNNaNNaNNaNNaNNaNNaNNaN40.707336-73.943203(40.707335577687445, -73.94320294670423)17213.036.02.030.056.0
95954810111/25/2023 01:26:20 PMNaNDPRDepartment of Parks and RecreationNew Tree RequestFor One AddressStreet11215.0437 2 STREET2 STREET6 AVENUE7 AVENUE6 AVENUE7 AVENUEADDRESSBROOKLYN2 STREETNaNIn ProgressNaNNaNNaN06 BROOKLYN3.009710e+09BROOKLYN989873.0184080.0UNKNOWNUnspecifiedBROOKLYNNaNNaNNaNNaNNaNNaNNaN40.671932-73.979729(40.67193203834086, -73.97972945430246)17617.014.02.027.050.0
Unique Keycreated_dateclosed_dateAgencyagency_nameComplaint TypeDescriptorlocation_typeincident_zipIncident AddressStreet NameCross Street 1Cross Street 2Intersection Street 1Intersection Street 2Address TypeCityLandmarkFacility TypeStatusDue DateResolution DescriptionResolution Action Updated DateCommunity BoardBBLBoroughX Coordinate (State Plane)Y Coordinate (State Plane)Open Data Channel TypePark Facility NamePark BoroughVehicle TypeTaxi Company BoroughTaxi Pick Up LocationBridge Highway NameBridge Highway DirectionRoad RampBridge Highway SegmentLatitudeLongitudeLocationzip_codesCommunity DistrictsBorough BoundariesCity Council DistrictsPolice Precincts
1442163231614301/02/2016 06:19:59 AMNaNDPRDepartment of Parks and RecreationNew Tree RequestFor One AddressStreet10028.0106 EAST 85 STREETEAST 85 STREETPARK AVENUELEXINGTON AVENUENaNNaNADDRESSNEW YORKNaNNaNAssigned07/02/2016 07:49:42 AMThe Department of Parks and Recreation has reviewed this request and will visit the location to investigate the condition.01/04/2016 08:05:13 AM08 MANHATTAN1.015130e+09MANHATTAN996101.0223192.0OTHERUnspecifiedMANHATTANNaNNaNNaNNaNNaNNaNNaN40.779279-73.957209(40.779278800779096, -73.95720904981226)12425.023.04.051.011.0
1442173231457001/02/2016 06:19:24 AM01/04/2016 08:05:35 AMDPRDepartment of Parks and RecreationNew Tree RequestFor One AddressStreet10028.0114 EAST 85 STREETEAST 85 STREETPARK AVENUELEXINGTON AVENUENaNNaNADDRESSNEW YORKNaNNaNClosed06/30/2016 06:19:24 AMThe agency has declined the new tree request because the suggested location cannot be planted due to infrastructure conflicts.01/04/2016 08:05:35 AM08 MANHATTAN1.015130e+09MANHATTAN996159.0223160.0OTHERUnspecifiedMANHATTANNaNNaNNaNNaNNaNNaNNaN40.779191-73.957000(40.7791908912874, -73.95699968329998)12425.023.04.051.011.0
1442183231240901/02/2016 12:14:10 AM01/17/2017 12:00:23 PMDPRDepartment of Parks and RecreationNew Tree RequestFor One AddressStreet11234.01384 EAST 49TH STREETEAST 49TH STREETNaNNaNNaNNaNLATLONGBROOKLYNNaNNaNClosed07/03/2016 12:01:57 PMThe Department of Parks and Recreation has determined that the requested location cannot receive a new tree because of conflicts with surrounding infrastructure.01/17/2017 12:00:23 PM01 BROOKLYNNaNBROOKLYN1003938.0167813.0MOBILEUnspecifiedBROOKLYNNaNNaNNaNNaNNaNNaNNaN40.627263-73.929074(40.62726256237021, -73.92907354860395)13825.05.02.026.038.0
1442193231116201/01/2016 08:01:18 PM01/04/2016 01:31:29 PMDPRDepartment of Parks and RecreationNew Tree RequestFor One AddressStreet11238.0165 PARK PLACEPARK PLACECARLTON AVENUEVANDERBILT AVENUENaNNaNADDRESSBROOKLYNNaNNaNClosed06/29/2016 08:01:18 PMThe agency has declined the new tree request because the suggested location cannot be planted due to infrastructure conflicts.01/04/2016 01:31:29 PM08 BROOKLYN3.011580e+09BROOKLYN992053.0186057.0PHONEUnspecifiedBROOKLYNNaNNaNNaNNaNNaNNaNNaN40.677357-73.971868(40.67735680515911, -73.97186840914296)13829.016.02.048.050.0
1442203231238401/01/2016 07:09:47 PMNaNDPRDepartment of Parks and RecreationNew Tree RequestFor One AddressStreet11694.0216 BEACH 144 STREETBEACH 144 STREETROCKAWAY BEACH BOULEVARDNEPONSIT AVENUENaNNaNADDRESSROCKAWAY PARKNaNNaNAssigned07/02/2016 08:14:38 AMThe Department of Parks and Recreation has reviewed this request and will visit the location to investigate the condition.01/04/2016 08:14:50 AM14 QUEENS4.163080e+09QUEENS1023054.0147537.0OTHERUnspecifiedQUEENSNaNNaNNaNNaNNaNNaNNaN40.571546-73.860324(40.57154601174077, -73.86032429911937)20532.051.03.041.058.0
1442213231187301/01/2016 03:47:13 PMNaNDPRDepartment of Parks and RecreationNew Tree RequestFor One AddressNaN10314.041 WILD AVENUEWILD AVENUENaNNaNNaNNaNLATLONGSTATEN ISLANDNaNNaNAssigned07/05/2016 12:57:08 PMThe location has been inspected and is scheduled for planting during the fall planting season. The fall planting season runs from approximately October to December, weather permitting.09/12/2018 11:01:43 AM02 STATEN ISLANDNaNSTATEN ISLAND931307.0155336.0MOBILEUnspecifiedSTATEN ISLANDNaNNaNNaNNaNNaNNaNNaN40.592880-74.190630(40.592879516752724, -74.19063024025972)10700.030.01.014.075.0
1442223231280901/01/2016 01:40:47 PM02/22/2016 05:49:38 PMDPRDepartment of Parks and RecreationNew Tree RequestFor One AddressStreet11237.0311 WEIRFIELD STREETWEIRFIELD STREETKNICKERBOCKER AVENUEIRVING AVENUENaNNaNADDRESSBROOKLYNNaNNaNClosed07/02/2016 01:29:37 PMThe Department of Parks and Recreation has determined that the requested location cannot receive a new tree because of conflicts with surrounding infrastructure.02/22/2016 05:49:38 PM04 BROOKLYN3.033990e+09BROOKLYN1009666.0192109.0OTHERUnspecifiedBROOKLYNNaNNaNNaNNaNNaNNaNNaN40.693935-73.908347(40.6939351044285, -73.9083467368299)13828.042.02.037.053.0
1442233231115001/01/2016 01:10:22 PMNaNDPRDepartment of Parks and RecreationNew Tree RequestFor One AddressStreet10011.0325 WEST 21 STREETWEST 21 STREET8 AVENUE9 AVENUENaNNaNADDRESSNEW YORKNaNNaNAssigned05/27/2017 01:22:46 PMThe Department of Parks and Recreation has reviewed this request and will visit the location to investigate the condition.11/28/2016 01:23:27 PM04 MANHATTAN1.007450e+09MANHATTAN984158.0210482.0OTHERUnspecifiedMANHATTANNaNNaNNaNNaNNaNNaNNaN40.744401-74.000332(40.74440106273172, -74.0003320147093)12074.012.04.010.06.0
1442243231238501/01/2016 12:40:50 PM03/04/2016 02:33:21 PMDPRDepartment of Parks and RecreationNew Tree RequestFor One AddressStreet11414.090-28 SHORE PARKWAYSHORE PARKWAY90 STREETBELT PARKWAY EXIT 17 EASTBOUNDNaNNaNADDRESSHOWARD BEACHNaNNaNClosed07/02/2016 08:04:57 AMThe Department of Parks and Recreation has determined that the requested location cannot receive a new tree because of conflicts with surrounding infrastructure.03/04/2016 02:33:21 PM10 QUEENS4.114720e+09QUEENS1027695.0181450.0OTHERUnspecifiedQUEENSNaNNaNNaNNaNNaNNaNNaN40.664608-73.843401(40.66460847040166, -73.84340074171881)15314.062.03.041.064.0
1442253231194401/01/2016 09:35:43 AM01/04/2016 01:29:15 PMDPRDepartment of Parks and RecreationNew Tree RequestFor One AddressStreet11223.0405 AVENUE TAVENUE TEAST 1 STREETEAST 2 STREETNaNNaNADDRESSBROOKLYNNaNNaNClosed06/29/2016 09:35:43 AMThe agency has declined the new tree request because the suggested location cannot be planted due to infrastructure conflicts.01/04/2016 01:29:15 PM15 BROOKLYN3.071040e+09BROOKLYN992510.0157735.0PHONEUnspecifiedBROOKLYNNaNNaNNaNNaNNaNNaNNaN40.599619-73.970255(40.59961856018033, -73.97025549178916)18183.032.02.045.036.0